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: "From Data Distributions to Regularization in Invariant Learning"
  Abstract: Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing the training data to include examples of inputs transformed by elements of G, while leaving the corresponding targets unchanged. Alternatively the cost function for training can include a regularization term that penalizes changes in the output when the input is transformed under the group. This paper relates the two approaches, showing precisely the sense in which the regularized cost function approximates the result of adding transformed (or distorted) examples to the training data. The cost function for the enhanced training set is equivalent to the sum of the original cost function plus a regularizer. For unbiased models, the regularizer reduces to the intuitively obvious choice - a term that penalizes changes in the output when the inputs are transformed under the group. For infinitesimal transformations, the coefficient of the regularization term reduces to the variance of the distortions introduced into the training data. This correspondence provides a simple bridge between the two approaches.

Connected through hyperedge HE-1 (induced by source paper "Face Recognition: A Hybrid Neural Network Approach"):
  with members:
    - "Evaluation of Pattern Classifiers for Fingerprint and OCR Applications"
    - "Human Face Detection in Visual Scenes"

Connected through hyperedge HE-2 (induced by source paper "Adaptive Noise Injection for Input Variables Relevance Determination"):
  with members:
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Least Absolute Shrinkage is Equivalent to Quadratic Penalization"
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"

Connected through hyperedge HE-3 (induced by source paper "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Regularization Theory and Neural Networks Architectures"

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