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: "Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control"
  Abstract: In this paper, we propose a memory-based Q-learning algorithm called predictive Q-routing (PQ-routing) for adaptive traffic control. We attempt to address two problems encountered in Q-routing (Boyan & Littman, 1994), namely, the inability to fine-tune routing policies under low network load and the inability to learn new optimal policies under decreasing load conditions. Unlike other memory-based reinforcement learning algorithms in which memory is used to keep past experiences to increase learning speed, PQ-routing keeps the best experiences learned and reuses them by predicting the traffic trend. The effectiveness of PQ-routing has been verified under various network topologies and traffic conditions. Simulation results show that PQ-routing is superior to

Connected through hyperedge HE-1 (induced by source paper "A Distributed Reinforcement Learning Scheme for Network Routing"):
  with members:
    - "Parameterized Heuristics for Intelligent Adaptive Network Routing in Large Communication Networks"

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