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: "A guide to the literature on learning probabilistic networks from data"
  Abstract: This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.

Connected through hyperedge HE-1 (induced by source paper "Learning Belief Networks from Data: An Information Theory Based Approach"):
  with members:
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"
    - "An Algorithm for Bayesian Belief Network Construction from Data"

Connected through hyperedge HE-2 (induced by source paper "An Algorithm for Bayesian Belief Network Construction from Data"):
  with members:
    - "Learning Belief Networks from Data: An Information Theory Based Approach"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"

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: Probabilistic_Methods