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: "PAC Analyses of a `Similarity Learning' IBL Algorithm"
  Abstract: V S-CBR [14] is a simple instance-based learning algorithm that adjusts a weighted similarity measure as well as collecting cases. This paper presents a `PAC' analysis of V S-CBR, motivated by the PAC learning framework, which demonstrates two main ideas relevant to the study of instance-based learners. Firstly, the hypothesis spaces of a learner on different target concepts can be compared to predict the difficulty of the target concepts for the learner. Secondly, it is helpful to consider the `constituent parts' of an instance-based learner: to explore separately how many examples are needed to infer a good similarity measure and how many examples are needed for the case base. Applying these approaches, we show that V S-CBR learns quickly if most of the variables in the representation are irrelevant to the target concept and more slowly if there are more relevant variables. The paper relates this overall behaviour to the behaviour of the constituent parts of V S-CBR.

Connected through hyperedge HE-1 (induced by source paper "Average-Case Analysis of a Nearest Neighbor Algorithm"):
  with members:
    - "Oblivious Decision Trees and Abstract Cases"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Inductive Bias in Case-Based Reasoning Systems"

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:
    - "Formalising the knowledge content of case memory systems"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"
    - "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 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"
    - "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: Theory