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: "Probabilistic Principal Component Analysis"
  Abstract: Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.

Connected through hyperedge HE-1 (induced by source paper "A Hierarchical Latent Variable Model for Data Visualization"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Mixtures of Probabilistic Principal Component Analysers"

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"
    - "The EM Algorithm for Mixtures of Factor Analyzers"

Connected through hyperedge HE-3 (induced by source paper "Mixtures of Probabilistic Principal Component Analysers"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "EM Algorithms for PCA and SPCA"
    - "A Hierarchical Latent Variable Model for Data Visualization"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"
    - "In  Fast Non-Linear Dimension Reduction"

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