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: "Hierarchical Explanation-Based Reinforcement Learning"
  Abstract: Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with the generalization ability of Explanation-Based Learning (EBL) (Di-etterich & Flann, 1995). We extend this work to domains where the agent must order and achieve a sequence of subgoals in an optimal fashion. Hierarchical EBRL can effectively learn optimal policies in some of these sequential task domains even when the subgoals weakly interact with each other. We also show that when a planner that can achieve the individual subgoals is available, our method converges even faster.

Connected through hyperedge HE-1 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"):
  with members:
    - "The Efficient Learning of Multiple Task Sequences"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Robust Reinforcement Learning in Motion Planning"
    - "Learning to Achieve Goals"
    - "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria"
    - "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"
    - "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"):
  with members:
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"

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