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 in Continuous Domains with Delayed Rewards"
  Abstract: Much has been done to develop learning techniques for delayed reward problems in worlds where the actions and/or states are approximated by discrete representations. Although this is acceptable in some applications there are many more situations where such an approximation is difficult and unnatural. For instance, in applications such as robotic,s where real machines interact with the real world, learning techniques that use real valued continuous quantities are required. Presented in this paper is an extension to Q-learning that uses both real valued states and actions. This is achieved by introducing activation strengths to each actuator system of the robot. This allow all actuators to be active to some continuous amount simultaneously. Learning occurs by incrementally adapting both the expected future reward to goal evaluation function and the gradients of that function with respect to each actuator system.

Connected through hyperedge HE-1 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Fast Online Q()"
    - "Modeling the Student with Reinforcement Learning"
    - "Generality versus Size in Genetic Programming"

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"
    - "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 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