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 "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.

Includes members:
  Member "Hidden Markov decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Factorial Hidden Markov Models")
  Member "On Structured Variational Approximations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models")
  Member "Computing upper and lower bounds on likelihoods in intractable networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "database")
  Member "Recursive algorithms for approximating probabilities in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction")
  Member "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Factorial Hidden Markov Models")
  Member "Learning in Boltzmann Trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Decimatable Boltzmann Machines vs. Gibbs Sampling")
  Member "Implicit learning in 3D object recognition: The importance of temporal context":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Viewpoint invariant face recognition using independent component analysis and attractor networks")
  Member "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")

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