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: "Massive Data Discrimination via Linear Support Vector Machines"
  Abstract: A linear support vector machine formulation is used to generate a fast, finitely-terminating linear-programming algorithm for discriminating between two massive sets in n-dimensional space, where the number of points can be orders of magnitude larger than n. The algorithm creates a succession of sufficiently small linear programs that separate chunks of the data at a time. The key idea is that a small number of support vectors, corresponding to linear programming constraints with positive dual variables, are carried over between the successive small linear programs, each of which containing a chunk of the data. We prove that this procedure is monotonic and terminates in a finite number of steps at an exact solution that leads to a globally optimal separating plane for the entire dataset. Numerical results on fully dense publicly available datasets, numbering 20,000 to 1 million points in 32-dimensional space, confirm the theoretical results and demonstrate the ability to handle very large problems.

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"
    - "Improving the Accuracy and Speed of Support Vector Machines"
    - "Parsimonious Least Norm Approximation"

Connected through hyperedge HE-2 (induced by source paper "Support Vector Machines: Training and Applications"):
  with members:
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "Human Face Detection in Visual Scenes"
    - "Extracting Support Data for a Given Task"

Connected through hyperedge HE-3 (induced by source paper "Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV 1"):
  with members:
    - "Ridge Regression Learning Algorithm in Dual Variables"

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