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: "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.

Connected through hyperedge HE-1 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Learning in Boltzmann Trees"
    - "Theory of Correlations in Stochastic Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "database"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-3 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks"):
  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"
    - "Using Qualitative Relationships for Bounding Probability Distributions"

Connected through hyperedge HE-4 (induced by source paper "A variational approach to Bayesian logistic regression models and their extensions"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-5 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "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-6 (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"

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