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: "Knowledge Compilation and Speedup Learning in Continuous Task Domains"
  Abstract: Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use continuous operators that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimo-tor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimen tation.

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"
    - "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 "Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters"):
  with members:
    - "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"
    - "What Kind of Adaptation do CBR Systems Need?: A Review of Current Practice"

Connected through hyperedge HE-3 (induced by source paper "Continuous Case-Based Reasoning"):
  with members:
    - "MULTISTRATEGY LEARNING IN REACTIVE CONTROL SYSTEMS FOR AUTONOMOUS ROBOTIC NAVIGATION"
    - "Systematic Evaluation of Design Decisions in CBR Systems"

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