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: "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"
  Abstract: This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schema-based reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system's environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line case learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations.

Connected through hyperedge HE-1 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"

Connected through hyperedge HE-2 (induced by source paper "Clay: Integrating Motor Schemas and Reinforcement Learning"):
  with members:
    - "Learning Roles: Behavioral Diversity in Robot Teams"

Connected through hyperedge HE-3 (induced by source paper "A Case-Based Approach to Reactive Control for Autonomous Robots"):
  with members:
    - "Using Case-Based Reasoning for Mobile Robot Navigation"

Connected through hyperedge HE-4 (induced by source paper "Systematic Evaluation of Design Decisions in CBR Systems"):
  with members:
    - "Generalizing from Case Studies: A Case Study"
    - "Structure oriented case retrieval"
    - "Continuous Case-Based Reasoning"

Connected through hyperedge HE-5 (induced by source paper "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"):
  with members:
    - "Knowledge Compilation and Speedup Learning in Continuous Task Domains"
    - "What Kind of Adaptation do CBR Systems Need?: A Review of Current Practice"

Connected through hyperedge HE-6 (induced by source paper "Knowledge Compilation and Speedup Learning in Continuous Task Domains"):
  with members:
    - "Continuous Case-Based Reasoning"
    - "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"

Connected through hyperedge HE-7 (induced by source paper "Continuous Case-Based Reasoning"):
  with members:
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Knowledge Compilation and Speedup Learning in Continuous Task Domains"

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