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 "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.

Includes members:
  Member "Generative Models for Discovering Sparse Distributed Representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Data Mining for Association Rules with Unsupervised Neural Networks")
  Member "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Convergence properties of perturbed Markov chains")
  Member "Discovering Structure in Continuous Variables Using Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")

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