I think this can be solved by recasting it in the language of Lagrange multipliers. We wish to maximize the function $f(x,y,z) = z$ subject to the constraints $g_1(x,y,z) = x^y - z= 0$ and $g_2(x,y,z) = y^x - z = 0$. By the properties of Lagrange multipliers, $f$ will have a local extremum on the surface specified by these constraints if there exists a point where
$$
\nabla f + \lambda_1 \nabla g_1 + \lambda_2 \nabla g_2 = 0.
$$
Taking these gradients, we find that we must have
\begin{align*}
\lambda_1 [y x^{y-1}] + \lambda_2 [(\ln y) y^x] &= 0 \\
\lambda_1 [(\ln x) x^y] + \lambda_2 [x y^{x-1}] &= 0 \\
1 -\lambda_1 - \lambda_2 &= 0
\end{align*}
We can then eliminate $\lambda_2$ from these equations:
\begin{align*}
\lambda_1 [y x^{y-1}] + (1 - \lambda_1) [(\ln y) y^x] &= 0 \\
\lambda_1 [(\ln x) x^y] + (1 - \lambda_1) [x y^{x-1}] &= 0
\end{align*}
Then we can solve each equation for $\lambda_1$ to eliminate it from our system:
$$
\lambda_1 = \frac{(\ln y) y^x}{(\ln y) y^x - y x^{y-1}} = \frac{x y^{x-1}}{x y^{x-1} - (\ln x) x^y }
$$
Inverting both sides of this equation and simplifying yields
$$
\frac{y x^{y-1}}{(\ln y) y^x} = \frac{(\ln x) x^y }{x y^{x-1}},
$$
which further simplifies to
$$
(\ln x) (\ln y) = 1.
$$
Thus, at the extremum (if it exists), we must have $y = e^{1/\ln x}$ and $z = y^x = e^{x/\ln x}.$ But on the interval $(1, \infty)$, the function $x / \ln x$ has a global minimum at $x = e$. Moreover, since the exponential function is monotonically increasing, the function $z = e^{x/\ln x}$ has a global minimum at $x = e$ as well. Thus, $z \geq e^e$, with equality at the point $x = y = e$.