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: "Discretization of continuous attributes using ReliefF"
  Abstract: Many existing learning algorithms expect the attributes to be discrete. Discretization of continuous attributes might be difficult task even for domain experts. We have tried the non-myopic heuristic measure ReliefF for discretization and compared it with well known dissimilarity measure and discretizations by experts. An extensive testing with several learning algorithms on six real world databases has shown that none of the discretizations has clear advantage over the others.

Connected through hyperedge HE-1 (induced by source paper "Induction of decision trees using RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"

Connected through hyperedge HE-2 (induced by source paper "Linear Space Induction in First Order Logic with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-3 (induced by source paper "Estimating Attributes: Analysis and Extensions of RELIEF"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Context-sensitive attribute estimation in regression"
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Naive Bayesian classifier within ILP-R"

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