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: "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"
  Abstract: The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the

Connected through hyperedge HE-1 (induced by source paper "Formalising the knowledge content of case memory systems"):
  with members:
    - "A Formal Analysis of Case Base Retrieval"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"

Connected through hyperedge HE-2 (induced by source paper "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"):
  with members:
    - "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-3 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"):
  with members:
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"

Connected through hyperedge HE-4 (induced by source paper "A Yardstick for the Evaluation of Case-Based Classifiers"):
  with members:
    - "Formalising the knowledge content of case memory systems"
    - "The Power of Decision Tables"
    - "Inductive Bias in Case-Based Reasoning Systems"

Connected through hyperedge HE-5 (induced by source paper "PAC Analyses of a `Similarity Learning' IBL Algorithm"):
  with members:
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"

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