human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Average-Case Analysis of a Nearest Neighbor Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems")
  Member "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the")
  Member "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Formalising the knowledge content of case memory systems")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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