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 "Computing upper and lower bounds on likelihoods in intractable networks"):
  Abstract: We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of the network (or clique size) precludes exact computations. We illustrate the tightness of the bounds by numerical experi ments.

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 "database":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning")
  Member "A variational approach to Bayesian logistic regression models and their extensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Mean Field Theory for Sigmoid Belief Networks")
  Member "Recursive algorithms for approximating probabilities in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction")
  Member "Using Qualitative Relationships for Bounding Probability Distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions")

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