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: "Learning To Play the Game of Chess"
  Abstract: This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial neural networks. It integrates inductive neural network learning, temporal differencing, and a variant of explanation-based learning. Performance results illustrate some of the strengths and weaknesses of this approach.

Connected through hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search."):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"

Connected through hyperedge HE-2 (induced by source paper "Generalization in Reinforcement Learning: Safely Approximating the Value Function"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Issues in Using Function Approximation for Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Issues in Using Function Approximation for Reinforcement Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Locally Weighted Learning for Control"

Connected through hyperedge HE-4 (induced by source paper "Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,"

Connected through hyperedge HE-5 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

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