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 Distributed Reinforcement Learning Scheme for Network Routing"
  Abstract: In this paper we describe a self-adjusting algorithm for packet routing in which a reinforcement learning method is embedded into each node of a network. Only local information is used at each node to keep accurate statistics on which routing policies lead to minimal routing times. In simple experiments involving a 36-node irregularly-connected network, this learning approach proves superior to routing based on precomputed shortest paths.

Connected through hyperedge HE-1 (induced by source paper "Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Parameterized Heuristics for Intelligent Adaptive Network Routing in Large Communication Networks"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

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