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: "On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs"
  Abstract: Graphical Markov models use undirected graphs (UDGs), acyclic directed graphs (ADGs), or (mixed) chain graphs to represent possible dependencies among random variables in a multivariate distribution. Whereas a UDG is uniquely determined by its associated Markov model, this is not true for ADGs or for general chain graphs (which include both UDGs and ADGs as special cases). This paper addresses three questions regarding the equivalence of graphical Markov models: when is a given chain graph Markov equivalent (1) to some UDG? (2) to some (at least one) ADG? (3) to some decomposable UDG? The answers are obtained by means of an extension of Frydenberg's (1990) elegant graph-theoretic characterization of the Markov equivalence of chain graphs.

Connected through hyperedge HE-1 (induced by source paper "Chain graphs for learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

Connected through hyperedge HE-2 (induced by source paper "An Alternative Markov Property for Chain Graphs"):
  with members:
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"

Connected through hyperedge HE-3 (induced by source paper "A Graphical Characterization of Lattice Conditional Independence Models"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"

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