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: "A Support Vector Machine Approach to Decision Trees"
  Abstract: Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The "optimal" decision tree is characterized, and both a primal and dual space formulation for constructing the tree are proposed. The result is a method for generating logically simple decision trees with multivariate linear or nonlinear decisions. The preliminary results indicate that the method produces simple trees that generalize well with respect to other decision tree algorithms and single support vector machines.

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

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

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"

Connected through hyperedge HE-4 (induced by source paper "Parsimonious Least Norm Approximation"):
  with members:
    - "Feature Selection via Mathematical Programming"
    - "Mathematical Programming in Data Mining"

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