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 "Recursive algorithms for approximating probabilities in graphical models"):
  Abstract: MIT Computational Cognitive Science Technical Report 9604 Abstract We develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Yet the formalism can be straightforwardly integrated with exact methods whenever they are/become applicable. The approximations we use are controlled: they maintain consistently upper and lower bounds on the desired quantities at all times. We show that Boltzmann machines, sigmoid belief networks, or any combination (i.e., chain graphs) can be handled within the same framework. The accuracy of the methods is verified exper imentally.

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 "Computing upper and lower bounds on likelihoods in intractable networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "database")
  Member "Exploiting Tractable Substructures in Intractable Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View")
  Member "database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks")
  Member "A variational approach to Bayesian logistic regression models and their extensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks")
  Member "Boltzmann Machine learning using mean field theory and linear response correction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Theory of Correlations in Stochastic Neural Networks")

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