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: "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
  Abstract: To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization.

Connected through hyperedge HE-1 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Finding Structure in Reinforcement Learning"
    - "Value Function Based Production Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"
    - "Learning to Predict User Operations for Adaptive Scheduling"

Connected through hyperedge HE-2 (induced by source paper "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"):
  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,"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-3 (induced by source paper "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "Generalized Prioritized Sweeping"
    - "Dynamic Belief Networks for Discrete Monitoring"

Connected through hyperedge HE-4 (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,"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"
    - "Spurious Solutions to the Bellman Equation"
    - "Metrics for Temporal Difference Learning"
    - "Residual Q-Learning Applied to Visual Attention"

Connected through hyperedge HE-5 (induced by source paper "Learning To Play the Game of Chess"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Issues in Using Function Approximation for Reinforcement Learning"
    - "TDLeaf(): Combining Temporal Difference learning with game-tree search."
    - "NeuroDraughts: the role of representation, search, training regime and architecture in a TD draughts player"
    - "KnightCap: A chess program that learns by combining TD() with minimax search"
    - "Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches"

Connected through hyperedge HE-6 (induced by source paper "Decision Tree Function Approximation in Reinforcement Learning"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"

Connected through hyperedge HE-7 (induced by source paper "Generality versus Size in Genetic Programming"):
  with members:
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"
    - "Evolving Visual Routines"
    - "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited"

Connected through hyperedge HE-8 (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,"
    - "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"
    - "Learning To Play the Game of Chess"
    - "Locally Weighted Learning for Control"

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