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: "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"
  Abstract: This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible performance in novel environments without extensive high-level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (A Case-BAsed Reactive Robotic) system, and evaluated through empirical simulation of the system on several different environments, including "box canyon" environments known to be problematic for reactive control systems in general. fl Technical Report GIT-CC-92/57, College of Computing, Georgia Institute of Technology, Atlanta, Geor gia, 1992.

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"
    - "A Case-Based Approach to Reactive Control for Autonomous Robots"

Connected through hyperedge HE-2 (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"
    - "Continuous Case-Based Reasoning"

Connected through hyperedge HE-3 (induced by source paper "What Kind of Adaptation do CBR Systems Need?: A Review of Current Practice"):
  with members:
    (none)

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