human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Spurious Solutions to the Bellman Equation"):
  Abstract: Reinforcement learning algorithms often work by finding functions that satisfy the Bellman equation. This yields an optimal solution for prediction with Markov chains and for controlling a Markov decision process (MDP) with a finite number of states and actions. This approach is also frequently applied to Markov chains and MDPs with infinite states. We show that, in this case, the Bellman equation may have multiple solutions, many of which lead to erroneous predictions and policies (Baird, 1996). Algorithms and conditions are presented that guarantee a single, optimal solution to the Bellman equation.

Includes members:
  Member "In  Improving Elevator Performance Using Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning")
  Member "MultiPlayer Residual Advantage Learning With General Function Approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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