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: "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.

Connected through hyperedge HE-1 (induced by source paper "database"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Recursive algorithms for approximating probabilities in graphical models"

Connected through hyperedge HE-2 (induced by source paper "A variational approach to Bayesian logistic regression models and their extensions"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Recursive algorithms for approximating probabilities in graphical models"

Connected through hyperedge HE-3 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Learning in Boltzmann Trees"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Tractable Inference for Complex Stochastic Processes"

Connected through hyperedge HE-4 (induced by source paper "Mean Field Theory for Sigmoid Belief Networks"):
  with members:
    - "Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence a free parameter. The"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Localist Attractor Networks"

Connected through hyperedge HE-5 (induced by source paper "Recursive algorithms for approximating probabilities in graphical models"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "database"
    - "A variational approach to Bayesian logistic regression models and their extensions"
    - "Boltzmann Machine learning using mean field theory and linear response correction"

Connected through hyperedge HE-6 (induced by source paper "Using Qualitative Relationships for Bounding Probability Distributions"):
  with members:
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Localized Partial Evaluation of Belief Networks"

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