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 "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"
    - "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"
    - "Formalising the knowledge content of case memory systems"

Connected through hyperedge HE-2 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"):
  with members:
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"
    - "Lazy Acquisition of Place Knowledge"
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "SaxEx a case-based reasoning system for generating expressive musical performances"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "NACODAE: Navy Conversational Decision Aids Environment"

Connected through hyperedge HE-3 (induced by source paper "Defining and Combining Symmetric and Asymmetric Similarity Measures"):
  with members:
    (none)

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