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 Act using Real-Time Dynamic Programming"
  Abstract: fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526).

Connected through hyperedge HE-1 (induced by source paper "On the Complexity of Solving Markov Decision Problems"):
  with members:
    - "Optimal Navigation in a Probibalistic World"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"

Connected through hyperedge HE-2 (induced by source paper "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Hierarchical Explanation-Based Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Improving Policies without Measuring Merits"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"

Connected through hyperedge HE-4 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "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-5 (induced by source paper "Value Function Based Production Scheduling"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"
    - "Efficient Locally Weighted Polynomial Regression Predictions"

Connected through hyperedge HE-6 (induced by source paper "Learning from an Automated Training Agent"):
  with members:
    - "An Introspection Approach to Querying a Trainer"

Connected through hyperedge HE-7 (induced by source paper "Robust Reinforcement Learning in Motion Planning"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"

Connected through hyperedge HE-8 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"):
  with members:
    - "A Cognitive Model of Learning to Navigate"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"

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