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: "Support Vector Machines: Training and Applications"
  Abstract: The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. The main idea behind the technique is to separate the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle [23]. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Since Structural Risk Minimization is an inductive principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the Mean Square Error over the data set (as Empirical Risk Minimization methods do), training a SVM to obtain the maximum margin classifier requires a different objective function. This objective function is then optimized by solving a large-scale quadratic programming problem with linear and box constraints. The problem is considered challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results in Frontal Human Face Detection in images. This application opens many interesting questions and future research opportunities, both in the context of faster and better optimization algorithms, and in the use of SVM's in other pattern classification, recognition, and detection applications. This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by MURI grant N00014-95-1-0600; by a grant from ONR/ARPA under contract N00014-92-J-1879 and by the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program). Edgar Osuna was supported by Fundacion Gran Mariscal de Ayacucho and Daimler Benz. Additional support is provided by Daimler-Benz, Eastman Kodak Company, Siemens Corporate Research, Inc. and AT&T.

Connected through hyperedge HE-1 (induced by source paper "Massive Data Discrimination via Linear Support Vector Machines"):
  with members:
    - "A Support Vector Machine Approach to Decision Trees"
    - "Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV 1"

Connected through hyperedge HE-2 (induced by source paper "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "A Sparse Representation for Function Approximation"
    - "Modeling Volatility using State Space Models"
    - "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"

Connected through hyperedge HE-3 (induced by source paper "Extracting Support Data for a Given Task"):
  with members:
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Improving the Accuracy and Speed of Support Vector Machines"
    - "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"
    - "Predicting Time Series with Support Vector Machines"
    - "Nonlinear Component Analysis as a Kernel Eigenvalue Problem"

Connected through hyperedge HE-4 (induced by source paper "Human Face Detection in Visual Scenes"):
  with members:
    - "Face Recognition: A Hybrid Neural Network Approach"
    - "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