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: "Mean Field Theory for Sigmoid Belief Networks"
  Abstract: We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition|the classification of handwritten digits.

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:
    - "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"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "Bayesian Unsupervised Learning of Higher Order Structure"):
  with members:
    - "A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS"
    - "A Brief History of Connectionism"

Connected through hyperedge HE-3 (induced by source paper "Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence a free parameter. The"):
  with members:
    - "Professor"

Connected through hyperedge HE-4 (induced by source paper "A MEAN FIELD LEARNING ALGORITHM FOR UNSUPERVISED NEURAL NETWORKS"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Bayesian Unsupervised Learning of Higher Order Structure"

Connected through hyperedge HE-5 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "Learning in Boltzmann Trees"
    - "Theory of Correlations in Stochastic Neural Networks"

Connected through hyperedge HE-6 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "database"
    - "A variational approach to Bayesian logistic regression models and their extensions"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "Using Qualitative Relationships for Bounding Probability Distributions"

Connected through hyperedge HE-7 (induced by source paper "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Factorial Hidden Markov Models"

Connected through hyperedge HE-8 (induced by source paper "Learning Generative Models with the Up-Propagation Algorithm"):
  with members:
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Pattern analysis and synthesis in attractor neural 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