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: "Continuous sigmoidal belief networks trained using slice sampling"
  Abstract: Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean Gaussian noise to its input before passing it through a sigmoidal squashing function. Such units can produce a variety of useful behaviors, ranging from deterministic to binary stochastic to continuous stochastic. I show how "slice sampling" can be used for inference and learning in top-down networks of these units and demonstrate learning on two simple problems.

Connected through hyperedge HE-1 (induced by source paper "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"):
  with members:
    - "Convergence properties of perturbed Markov chains"
    - "Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications"
    - "Analysis of a Non-Reversible Markov Chain Sampler"
    - "SUPPRESSING RANDOM WALKS IN MARKOV CHAIN MONTE CARLO USING ORDERED OVERRELAXATION"

Connected through hyperedge HE-2 (induced by source paper "Discovering Structure in Continuous Variables Using Bayesian Networks"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-3 (induced by source paper "Generative Models for Discovering Sparse Distributed Representations"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Pattern analysis and synthesis in attractor neural networks"
    - "The Rectified Gaussian Distribution"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Factor Analysis Using Delta-Rule Wake-Sleep Learning"

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