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: "Factor Analysis Using Delta-Rule Wake-Sleep Learning"
  Abstract: Technical Report No. 9607, Department of Statistics, University of Toronto We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake-sleep method, in which learning of the primary generative model is assisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Oja's version of Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plau sible alternative to Hebbian learning as a model of activity-dependent cortical plasticity.

Connected through hyperedge HE-1 (induced by source paper "Recognizing Handwritten Digits Using Mixtures of Linear Models"):
  with members:
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "In  Fast Non-Linear Dimension Reduction"
    - "The EM Algorithm for Mixtures of Factor Analyzers"
    - "Constraint Tangent Distance for On-line Character Recognition"

Connected through hyperedge HE-2 (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"
    - "Continuous sigmoidal belief networks trained using slice sampling"

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: Neural_Networks