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: "A Yardstick for the Evaluation of Case-Based Classifiers"
  Abstract: This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains.

Connected through hyperedge HE-1 (induced by source paper "Inductive Bias in Case-Based Reasoning Systems"):
  with members:
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-2 (induced by source paper "Formalising the knowledge content of case memory systems"):
  with members:
    - "A Formal Analysis of Case Base Retrieval"
    - "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"

Connected through hyperedge HE-3 (induced by source paper "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"):
  with members:
    - "Formalising the knowledge content of case memory systems"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "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-4 (induced by source paper "The Power of Decision Tables"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "Induction of Condensed Determinations"
    - "Decision Tree Induction Based on Efficient Tree Restructuring"
    - "Targeting Business Users with Decision Table Classifiers"

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