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: "Exploiting Tractable Substructures in Intractable Networks"
  Abstract: We develop a refined mean field approximation for inference and learning in probabilistic neural networks. Our mean field theory, unlike most, does not assume that the units behave as independent degrees of freedom; instead, it exploits in a principled way the existence of large substructures that are computationally tractable. To illustrate the advantages of this framework, we show how to incorporate weak higher order interactions into a first-order hidden Markov model, treating the corrections (but not the first order structure) within mean field theory.

Connected through hyperedge HE-1 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-2 (induced by source paper "Tractable Inference for Complex Stochastic Processes"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Factorial Hidden Markov Models"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-3 (induced by source paper "Implicit learning in 3D object recognition: The importance of temporal context"):
  with members:
    - "A self-organizing multiple-view representation of 3D objects"
    - "Viewpoint invariant face recognition using independent component analysis and attractor networks"
    - "A Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-4 (induced by source paper "On Structured Variational Approximations"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-5 (induced by source paper "Learning in Boltzmann Trees"):
  with members:
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Boltzmann Chains and Hidden Markov Models"
    - "Decimatable Boltzmann Machines vs. Gibbs Sampling"

Connected through hyperedge HE-6 (induced by source paper "Hidden Markov decision trees"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Factorial Hidden Markov Models"
    - "Coupled hidden Markov models for complex action recognition"

Connected through hyperedge HE-7 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks"):
  with members:
    - "Mean Field Theory for Sigmoid Belief 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-8 (induced by source paper "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Factorial Hidden Markov 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