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: "Clay: Integrating Motor Schemas and Reinforcement Learning"
  Abstract: Clay is an evolutionary architecture for autonomous robots that integrates motor schema-based control and reinforcement learning. Robots utilizing Clay benefit from the real-time performance of motor schemas in continuous and dynamic environments while taking advantage of adaptive reinforcement learning. Clay coordinates assemblages (groups of motor schemas) using embedded reinforcement learning modules. The coordination modules activate specific assemblages based on the presently perceived situation. Learning occurs as the robot selects assemblages and samples a reinforcement signal over time. Experiments in a robot soccer simulation illustrate the performance and utility of the system.

Connected through hyperedge HE-1 (induced by source paper "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Continuous Case-Based Reasoning"
    - "Knowledge Compilation and Speedup Learning in Continuous Task Domains"
    - "A Case-Based Approach to Reactive Control for Autonomous Robots"
    - "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"

Connected through hyperedge HE-2 (induced by source paper "Learning Roles: Behavioral Diversity in Robot Teams"):
  with members:
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"

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