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: "Learning Stochastic Feedforward Networks"
  Abstract: Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of stochastic feedforward network. These networks are in the same class as the "belief networks" used in expert systems. They represent a probability distribution over a set of visible variables using hidden variables to express correlations. Conditional probability distributions can be exhibited by stochastic simulation for use in tasks such as classification. Learning from empirical data is done via a gradient-ascent method analogous to that used in Boltzmann machines, but due to the feedforward nature of the connections, the negative phase of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid feedforward network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, and provide a link between work on connectionist learning and work on the representation of expert knowledge.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised learning of distributions on binary vectors using two layer networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Learning in Boltzmann Trees"
    - "Mapping Bayesian Networks to Boltzmann Machines"

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