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: "Formalising the knowledge content of case memory systems"
  Abstract: Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this `knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumlates problem-solving experience. The current paper presents a `case-base semantics' which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these sematics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base.

Connected through hyperedge HE-1 (induced by source paper "A Formal Analysis of Case Base Retrieval"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "The case for cases: a call for purity in case-based reasoning inherently more difficult than"

Connected through hyperedge HE-2 (induced by source paper "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"):
  with members:
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"
    - "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"

Connected through hyperedge HE-3 (induced by source paper "A Yardstick for the Evaluation of Case-Based Classifiers"):
  with members:
    - "The Power of Decision Tables"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"

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