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 Hierarchical Control Structures for Multiple Tasks and Changing Environments"
  Abstract: While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a difficult task. The benefit of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.

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 "Learning in Continuous Domains with Delayed Rewards"):
  with members:
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
    - "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"

Connected through hyperedge HE-3 (induced by source paper "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning in Continuous Domains with Delayed Rewards"

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