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: "Recognizing Handwritten Digits Using Mixtures of Linear Models"
  Abstract: We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images are classified by evaluating their log-likelihoods under each model. We use an EM-based algorithm in which the M-step is computationally straightforward principal components analysis (PCA). Incorporating tangent-plane information [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.

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"
    - "EM Algorithms for PCA and SPCA"

Connected through hyperedge HE-2 (induced by source paper "Constraint Tangent Distance for On-line Character Recognition"):
  with members:
    - "Adaptive Boosting of Neural Networks for Character Recognition"

Connected through hyperedge HE-3 (induced by source paper "In  Fast Non-Linear Dimension Reduction"):
  with members:
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "MBP on T0: mixing floating- and fixed-point formats in BP learning"

Connected through hyperedge HE-4 (induced by source paper "Data Mining for Association Rules with Unsupervised Neural Networks"):
  with members:
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "The EM Algorithm for Mixtures of Factor Analyzers"

Connected through hyperedge HE-5 (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"
    - "In  Fast Non-Linear Dimension Reduction"
    - "Probabilistic Principal Component Analysis"

Connected through hyperedge HE-6 (induced by source paper "Comparison of Neural and Statistical Classifiers| Theory and Practice"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "The Pandemonium System of Reflective Agents"
    - "Evaluation of Pattern Classifiers for Fingerprint and OCR Applications"

Connected through hyperedge HE-7 (induced by source paper "Factor Analysis Using Delta-Rule Wake-Sleep Learning"):
  with members:
    - "Generative Models for Discovering Sparse Distributed Representations"

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