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: "Constraint Tangent Distance for On-line Character Recognition"
  Abstract: In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of tangent distance [9], well known in off-line character recognition. The system has been implemented with a k-nearest neighbor classifier and a so called diabolo classifier [6] respectively. Both classifiers are invariant under transformations like rotation, scale or slope and can deal with variations in stroke order and writing direction. Results are presented for our digit database with more than 200 writers.

Connected through hyperedge HE-1 (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"
    - "In  Fast Non-Linear Dimension Reduction"
    - "The EM Algorithm for Mixtures of Factor Analyzers"

Connected through hyperedge HE-2 (induced by source paper "Adaptive Boosting of Neural Networks for Character Recognition"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Experiments with a New Boosting Algorithm"

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