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: "Multi-Strategy Learning and Theory Revision"
  Abstract: This paper presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a-priori knowledge consists of a causal model of the domain, stating the relationships among basic phenomena, and a body of phenomenological theory, describing the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and limited number of examples. The system works in a first order logic environment and has been applied in a real domain.

Connected through hyperedge HE-1 (induced by source paper "From Theory Refinement to KB Maintenance: a Position Statement"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"

Connected through hyperedge HE-2 (induced by source paper "Tractability of Theory Patching"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Bias-Driven Revision of Logical Domain Theories"

Connected through hyperedge HE-3 (induced by source paper "Knowledge Based Systems"):
  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