human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Neural network solution for finite-horizon H ∞ state feedback control of nonlinear systems"
  Abstract: In this article, neural networks are used to approximately solve the finite-horizon optimal H∞ state feedback control problem. The method is based on solving a related Hamilton Jacobi Isaacs equation of the corresponding finite-horizon zero-sum game. The neural network approximates the corresponding game value function on a certain domain of the state-space and results in a control computed as the output of a neural network. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting controller provides closed-loop stability and bounded L2 gain. The result is a nearly exact H∞ feedback controller with time-varying coefficients that is solved a priori offline. The results of this article are applied to the rotational/translational actuator benchmark nonlinear control problem.

Connected through hyperedge HE-1 (induced by source paper "A Neural Network Solution for Fixed-Final Time Optimal Control of Nonlinear Systems"):
  with members:
    (none)

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics