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: "The Bayesian Approach to Tree-Structured Regression"
  Abstract: In the context of inductive learning, the Bayesian approach turned out to be very successful in estimating probabilities of events when there are only a few learning examples. The m-probability estimate was developed to handle such situations. In this paper we present the m-distribution estimate, an extension to the m-probability estimate which, besides the estimation of probabilities, covers also the estimation of probability distributions. We focus on its application in the construction of regression trees. The theoretical results were incorporated into a system for automatic induction of regression trees. The results of applying the upgraded system to several domains are presented and compared to previous results.

Connected through hyperedge HE-1 (induced by source paper "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "Structural Regression Trees"
    - "An adaptation of Relief for attribute estimation in regression"
    - "First Order Regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Producing More Comprehensible Models While Retaining Their Performance"
    - "Context-sensitive attribute estimation in regression"

Connected through hyperedge HE-2 (induced by source paper "Drug design by machine learning: Modelling drug activity"):
  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