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: "Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on"
  Abstract: This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows.

Connected through hyperedge HE-1 (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-2 (induced by source paper "Rule Induction and Instance-Based Learning: A Unified Approach"):
  with members:
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"
    - "Rule Generation and Compaction in the wwtp"
    - "Inductive CBR for Customer Support"

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: Rule_Learning