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: "A Case-Based Approach to Reactive Control for Autonomous Robots"
  Abstract: We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive Robotic system) provides more flexible performance in novel environments, as well as overcoming a standard "hard" problem for reactive systems, the box canyon. Additionally, ACBARR is designed in a manner which is intended to remain as close to pure reactive control as possible. Higher level reasoning and memory functions are intentionally kept to a minimum. As a result, the new reasoning does not significantly slow the system down from pure reactive speeds.

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"
    - "Clay: Integrating Motor Schemas and Reinforcement Learning"
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Continuous Case-Based Reasoning"
    - "Knowledge Compilation and Speedup Learning in Continuous Task Domains"
    - "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 "Using Case-Based Reasoning for Mobile Robot Navigation"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"

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: Case_Based