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: "Estimating Dependency Structure as a Hidden Variable"
  Abstract: This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. This report describes research done at the Dept. of Electrical Engineering and Computer Science, the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense and by the Office of Naval Research. Michael I. Jordan is a NSF Presidential Young Investigator. The authors can be reached at M.I.T., Center for Biological and Computational Learning, 45 Carleton St., Cambridge MA 02142, USA. E-mail: mmp@ai.mit.edu, jordan@psyche.mit.edu, quaid@ai.mit.edu.

Connected through hyperedge HE-1 (induced by source paper "Constructing Bayesian finite mixture models by the EM algorithm"):
  with members:
    - "Comparing Predictive Inference Methods for Discrete Domains"

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