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: "IGLUE An Instance-based Learning System over Lattice Theory"
  Abstract: Concept learning is one of the most studied areas in machine learning. A lot of work in this domain deals with decision trees. In this paper, we are concerned with a different kind of technique based on Galois lattices or concept lattices. We present a new semi-lattice based system, IGLUE, that uses the entropy function with a top-down approach to select concepts during the lattice construction. Then IGLUE generates new relevant numerical features by transforming initial boolean features over these concepts. IGLUE uses the new features to redescribe examples. Finally, IGLUE applies the Mahanalobis distance as a similarity measure between examples. Keywords : Multistrategy Learning, Instance-Based Learning, Galois lattice, Feature transformation

Connected through hyperedge HE-1 (induced by source paper "Learning Classification Rules Using Lattices"):
  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