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: "The EM Algorithm for Mixtures of Factor Analyzers"
  Abstract: Technical Report CRG-TR-96-1 May 21, 1996 (revised Feb 27, 1997) Abstract Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing different local factor models in different regions of the input space. This results in a model which concurrently performs clustering and dimensionality reduction, and can be thought of as a reduced dimension mixture of Gaussians. We present an exact Expectation-Maximization algorithm for fitting the parameters of this mixture of factor analyzers.

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"
    - "Factor Analysis Using Delta-Rule Wake-Sleep Learning"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "In  Fast Non-Linear Dimension Reduction"
    - "Constraint Tangent Distance for On-line Character Recognition"

Connected through hyperedge HE-2 (induced by source paper "EM Algorithms for PCA and SPCA"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "Probabilistic Principal Component Analysis"

Connected through hyperedge HE-3 (induced by source paper "Data Mining for Association Rules with Unsupervised Neural Networks"):
  with members:
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-4 (induced by source paper "A HIERARCHICAL COMMUNITY OF EXPERTS"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"

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