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: "Robust performance and adaptation using receding horizon H 1 control of time varying systems."
  Abstract: In this paper we construct suboptimal H 1 controllers which satisfy a new robust performance condition, using the receding horizon technique. A method is described for the synthesis of H 1 controllers online, making use of the exact plant model only on a finite interval extending into the future. Inequalities based on the two Riccati differential equation solution to the finite horizon H 1 problem are derived, and the resulting freedom is exploited to construct H 1 controllers which have a closed loop induced norm less than a prespecified value for all plants within a set, which is described in terms of the future variation of the plant. Dual results, with a possible adaptive interpretation, are also constructed.

Connected through hyperedge HE-1 (induced by source paper "A game theoretic approach to moving horizon control"):
  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