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: "Face Recognition: A Hybrid Neural Network Approach"
  Abstract: Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult (Turk and Pentland, 1991). We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map neural network, and a convolutional neural network. The self-organizing map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the self-organizing map, and a multilayer perceptron in place of the convolutional network. The Karhunen-Loeve transform performs almost as well (5.3% error versus 3.8%). The multilayer perceptron performs very poorly (40% error versus 3.8%). The method is capable of rapid classification, requires only fast, approximate normalization and preprocessing, and consistently exhibits better classification performance than the eigenfaces approach (Turk and Pentland, 1991) on the database considered as the number of images per person in the training database is varied from 1 to 5. With 5 images per person the proposed method and eigenfaces result in 3.8% and 10.5% error respectively. The recognizer provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of the examples. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze computational complexity and discuss how new classes could be added to the trained recognizer.

Connected through hyperedge HE-1 (induced by source paper "From Data Distributions to Regularization in Invariant Learning"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"

Connected through hyperedge HE-2 (induced by source paper "Evaluation of Pattern Classifiers for Fingerprint and OCR Applications"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"

Connected through hyperedge HE-3 (induced by source paper "Human Face Detection in Visual Scenes"):
  with members:
    - "Support Vector Machines: Training and Applications"
    - "A Neural Network Based Head Tracking System"

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