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: "In  Fast Non-Linear Dimension Reduction"
  Abstract: We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train.

Connected through hyperedge HE-1 (induced by source paper "MBP on T0: mixing floating- and fixed-point formats in BP learning"):
  with members:
    - "Quicknet on MultiSpert: Fast Parallel Neural Network Training"

Connected through hyperedge HE-2 (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"
    - "The EM Algorithm for Mixtures of Factor Analyzers"
    - "Constraint Tangent Distance for On-line Character Recognition"

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"
    - "Probabilistic Principal Component Analysis"

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