human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "An Alternative Markov Property for Chain Graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian")
  Member "Chain graphs for learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")
  Member "A Graphical Characterization of Lattice Conditional Independence Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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