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: "Machine learning in blood group determination of Danish Jersey cattle (causal probabilistic network). Dobljene mreze"
  Abstract: In the following paper we approach the problem with different machine learning algorithms and show that they can be compared with causal probabilistic networks in terms of performance and comprehensibility.

Connected through hyperedge HE-1 (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