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: "Improving the Accuracy and Speed of Support Vector Machines"
  Abstract: Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization performance (the "virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine. The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance, achieving 1.1% error. The virtual support vector method is applicable to any SVM problem with known invariances. The reduced set method is applicable to any support vector machine.

Connected through hyperedge HE-1 (induced by source paper "A Support Vector Machine Approach to Decision Trees"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Parsimonious Least Norm Approximation"
    - "Massive Data Discrimination via Linear Support Vector Machines"

Connected through hyperedge HE-2 (induced by source paper "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Extracting Support Data for a Given Task"
    - "Incorporating Invariances in Support Vector Learning Machines"

Connected through hyperedge HE-3 (induced by source paper "Incorporating Invariances in Support Vector Learning Machines"):
  with members:
    - "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Extracting Support Data for a Given Task"):
  with members:
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers"
    - "Support Vector Machines: Training and Applications"
    - "Predicting Time Series with Support Vector Machines"
    - "Nonlinear Component Analysis as a Kernel Eigenvalue Problem"

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: Theory