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 "Factorial Learning and the EM Algorithm"):
  Abstract: Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discovering such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative vector quantizer (CVQ) architecture, an unsupervised learning algorithm is derived from the Expectation-Maximization (EM) framework. Due to the combinatorial nature of the data generation process, the exact E-step is computationally intractable. Two alternative methods for computing the E-step are proposed: Gibbs sampling and mean-field approximation, and some promising empirical results are presented.

Includes members:
  Member "Hierarchical Mixtures of Experts and the EM Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts")
  Member "Unsupervised Learning by Convex and Conic Coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Generative Models for Discovering Sparse Distributed Representations")

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