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: "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"
  Abstract: The use of externally imposed hierarchical structures to reduce the complexity of learning control is common. However, it is acknowledged that learning the hierarchical structure itself is an important step towards more general (learning of many things as required) and less bounded (learning of a single thing as specified) learning. Presented in this paper is a reinforcement learning algorithm called Nested Q-learning that generates a hierarchical control structure in reinforcement learning domains. The emergent structure combined with learned bottom-up reactive reactions results in a reactive hierarchical control system. Effectively, the learned hierarchy decomposes what would otherwise be a monolithic evaluation function into many smaller evaluation functions that can be recombined without the loss of previously learned information.

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"
    - "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"

Connected through hyperedge HE-2 (induced by source paper "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning in Continuous Domains with Delayed Rewards"

Connected through hyperedge HE-3 (induced by source paper "Learning in Continuous Domains with Delayed Rewards"):
  with members:
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
    - "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"

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