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: "EM Algorithms for PCA and SPCA"
  Abstract: I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.

Connected through hyperedge HE-1 (induced by source paper "The EM Algorithm for Mixtures of Factor Analyzers"):
  with members:
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-2 (induced by source paper "Supervised learning from incomplete data via an EM approach"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Recurrent Neural Networks for Missing or Asynchronous Data"
    - "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "A Statistical Approach to Decision Tree Modeling"
    - "Learning Bayesian Networks from Incomplete Data"

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"
    - "A Hierarchical Latent Variable Model for Data Visualization"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"
    - "In  Fast Non-Linear Dimension Reduction"
    - "Probabilistic Principal Component Analysis"

Connected through hyperedge HE-4 (induced by source paper "Probabilistic Principal Component Analysis"):
  with members:
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "A Hierarchical Latent Variable Model for Data Visualization"

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