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: "Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples"
  Abstract: This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented.

Connected through hyperedge HE-1 (induced by source paper "An Efficient Subsumption Algorithm for Inductive Logic Programming"):
  with members:
    - "What online Machine Learning can do for Knowledge Acquisition A Case Study"
    - "Efficient Algorithms for -Subsumption"
    - "Efficient -Subsumption based on Graph 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: Rule_Learning