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 "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"):
  Abstract: Mean field methods provide computationally efficient approximations to posterior probability distributions for graphical models. Simple mean field methods make a completely factorized approximation to the posterior, which is unlikely to be accurate when the posterior is multimodal. Indeed, if the posterior is multi-modal, only one of the modes can be captured. To improve the mean field approximation in such cases, we employ mixture models as posterior approximations, where each mixture component is a factorized distribution. We describe efficient methods for optimizing the parameters in these models.

Includes members:
  Member "Mean Field Theory for Sigmoid Belief Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")
  Member "Exploiting Tractable Substructures in Intractable Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View")
  Member "Factorial Hidden Markov Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS")

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