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: "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.

Connected through hyperedge HE-1 (induced by source paper "MultiPlayer Residual Advantage Learning With General Function Approximation"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"
    - "Metrics for Temporal Difference Learning"
    - "Residual Q-Learning Applied to Visual Attention"

Connected through hyperedge HE-2 (induced by source paper "In  Improving Elevator Performance Using Reinforcement Learning"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "NEUROCONTROL BY REINFORCEMENT LEARNING"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"

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