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: "Mixtures of Probabilistic Principal Component Analysers"
  Abstract: Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

Connected through hyperedge HE-1 (induced by source paper "In  Fast Non-Linear Dimension Reduction"):
  with members:
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"
    - "MBP on T0: mixing floating- and fixed-point formats in BP learning"

Connected through hyperedge HE-2 (induced by source paper "A Hierarchical Latent Variable Model for Data Visualization"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Probabilistic Principal Component Analysis"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "EM Algorithms for PCA and SPCA"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "The EM Algorithm for Mixtures of Factor Analyzers"
    - "Probabilistic Principal Component Analysis"

Connected through hyperedge HE-5 (induced by source paper "Probabilistic Principal Component Analysis"):
  with members:
    - "EM Algorithms for PCA and SPCA"
    - "A Hierarchical Latent Variable Model for Data Visualization"

Connected through hyperedge HE-6 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On learning hierarchical classifications"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"

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