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: "Explaining Anomalies as a Basis for Knowledge Base Refinement"
  Abstract: Explanations play a key role in operationalization-based anomaly detection techniques. In this paper we show that their role is not limited to anomaly detection; they can also be used for guiding automated knowledge base refinement. We introduce a refinement procedure which takes: (i) a small number of refinement rules (rather than test cases), and (ii) explanations constructed in an attempt to reveal the cause (or causes) for inconsistencies detected during the verification process, and returns rule revisions aiming to recover the consistency of the KB-theory. Inconsistencies caused by more than one anomaly are handled at the same time, which improves the efficiency of the refinement process.

Connected through hyperedge HE-1 (induced by source paper "Theory Refinement Combining Analytical and Empirical Methods"):
  with members:
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "Learning Analytically and Inductively"
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"

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