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: "Chain graphs for learning"

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"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs"):
  with members:
    - "An Alternative Markov Property for Chain Graphs"
    - "A Graphical Characterization of Lattice Conditional Independence Models"

Connected through hyperedge HE-3 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

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