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: "Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model"
  Abstract: Brendan J. Frey and Geoffrey E. Hinton 1997. Efficient stochastic source coding and an application to a Bayesian network source model. The Computer Journal 40, 157-165. In this paper, we introduce a new algorithm called "bits-back coding" that makes stochastic source codes efficient. For a given one-to-many source code, we show that this algorithm can actually be more efficient than the algorithm that always picks the shortest codeword. Optimal efficiency is achieved when codewords are chosen according to the Boltzmann distribution based on the codeword lengths. It turns out that a commonly used technique for determining parameters | maximum likelihood estimation | actually minimizes the bits-back coding cost when codewords are chosen according to the Boltzmann distribution. A tractable approximation to maximum likelihood estimation | the generalized expectation maximization algorithm | minimizes the bits-back coding cost. After presenting a binary Bayesian network model that assigns exponentially many codewords to each symbol, we show how a tractable approximation to the Boltzmann distribution can be used for bits-back coding. We illustrate the performance of bits-back coding using using nonsynthetic data with a binary Bayesian network source model that produces 2 60 possible codewords for each input symbol. The rate for bits-back coding is nearly one half of that obtained by picking the shortest codeword for each symbol.

Connected through hyperedge HE-1 (induced by source paper "A simple algorithm that discovers efficient perceptual codes"):
  with members:
    - "Free energy coding  In"

Connected through hyperedge HE-2 (induced by source paper "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "On Structured Variational Approximations"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Localist Attractor Networks"
    - "Sequential Update of Bayesian Network Structure"

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