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: "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
  Abstract: This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.

Connected through hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search."):
  with members:
    - "Learning To Play the Game of Chess"

Connected through hyperedge HE-2 (induced by source paper "A Comparison of Action Selection Learning Methods"):
  with members:
    - "A Cognitive Model of Learning to Navigate"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Forward models: Supervised learning with a distal teacher"

Connected through hyperedge HE-3 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling"):
  with members:
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "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-4 (induced by source paper "Learning from undiscounted delayed rewards"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Designing Neural Networks for Adaptive Control"

Connected through hyperedge HE-5 (induced by source paper "Least-Squares Temporal Difference Learning"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Gain Adaptation Beats Least Squares?"
    - "A Comparison of Direct and Model-Based Reinforcement Learning"

Connected through hyperedge HE-6 (induced by source paper "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Hierarchical Explanation-Based Reinforcement Learning"

Connected through hyperedge HE-7 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Issues in Using Function Approximation for Reinforcement Learning"
    - "Reinforcement Learning with Soft State Aggregation"
    - "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition"

Connected through hyperedge HE-8 (induced by source paper "Towards a Reactive Critic"):
  with members:
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Truncating Temporal Differences: On the Efficient Implementation of TD() for Reinforcement Learning"

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