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: "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
  Abstract: The use of graphs to represent independence structure in multivariate probability models has been pursued in a relatively independent fashion across a wide variety of research disciplines since the beginning of this century. This paper provides a brief overview of the current status of such research with particular attention to recent developments which have served to unify such seemingly disparate topics as probabilistic expert systems, statistical physics, image analysis, genetics, decoding of error-correcting codes, Kalman filters, and speech recognition with Markov models.

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"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Learning in Boltzmann Trees"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Tractable Inference for Complex Stochastic Processes"

Connected through hyperedge HE-3 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models"):
  with members:
    - "On Structured Variational Approximations"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Belief Propagation and Revision in Networks with Loops"
    - "Speech Recognition with Dynamic Bayesian Networks"

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