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: "Continuous Case-Based Reasoning"
  Abstract: Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as online sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (Self-Improving Navigation System). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.

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"
    - "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 "Systematic Evaluation of Design Decisions in CBR Systems"):
  with members:
    - "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"
    - "Generalizing from Case Studies: A Case Study"
    - "Structure oriented case retrieval"

Connected through hyperedge HE-3 (induced by source paper "Knowledge Compilation and Speedup Learning in Continuous Task Domains"):
  with members:
    - "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"
    - "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"

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