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: "Discovering Structure in Continuous Variables Using Bayesian Networks"
  Abstract: We study Bayesian networks for continuous variables using nonlinear conditional density estimators. We demonstrate that useful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on

Connected through hyperedge HE-1 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Chain graphs for learning"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "Continuous sigmoidal belief networks trained using slice sampling"):
  with members:
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"

Connected through hyperedge HE-3 (induced by source paper "A Tutorial on Learning With Bayesian Networks"):
  with members:
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Learning Bayesian Networks from Incomplete Data"
    - "Sequential Update of Bayesian Network Structure"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"
    - "On the Sample Complexity of Learning Bayesian Networks"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"

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