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 Reinforcement Learning with the MAXQ Value Function Decomposition"
  Abstract: This paper describes the MAXQ method for hierarchical reinforcement learning based on a hierarchical decomposition of the value function and derives conditions under which the MAXQ decomposition can represent the optimal value function. We show that for certain execution models, the MAXQ decomposition will produce better policies than Feudal Q learning.

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"
    - "Hierarchical Explanation-Based Reinforcement Learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"):
  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 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"

Connected through hyperedge HE-3 (induced by source paper "Reinforcement Learning with Hierarchies of Machines"):
  with members:
    (none)

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