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: "Learning Classification Rules Using Lattices"
  Abstract: This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The construction of lattices from data is initially discussed and the use of these structures in inducing classification rules is examined. The Rulearner system is shown to compare favorably with commonly used symbolic learning methods which use heursitics rather than an explicit map to guide their search through the rule space. Furthermore, our learning system is shown to be robust in the presence of noisy data. The Rulearner system is also capable of learning both decision lists as well as unordered rule sets and thus allows for comparisons of these different learning paradigms within the same algorithmic framework.

Connected through hyperedge HE-1 (induced by source paper "IGLUE An Instance-based Learning System over Lattice Theory"):
  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: Rule_Learning