human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "A game theoretic approach to moving horizon control"
  Abstract: A control law is constructed for a linear time varying system by solving a two player zero sum differential game on a moving horizon, the game being that which is used to construct an H 1 controller on a finite horizon. Conditions are given under which this controller results in a stable system and satisfies an infinite horizon H 1 norm bound. A risk sensitive formulation is used to provide a state estimator in the observation feedback case.

Connected through hyperedge HE-1 (induced by source paper "Robust performance and adaptation using receding horizon H 1 control of time varying systems."):
  with members:
    (none)

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Reinforcement_Learning