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: "Parsimonious Least Norm Approximation"
  Abstract: A theoretically justifiable fast finite successive linear approximation algorithm is proposed for obtaining a parsimonious solution to a corrupted linear system Ax = b + p, where the corruption p is due to noise or error in measurement. The proposed linear-programming-based algorithm finds a solution x by parametrically minimizing the number of nonzero elements in x and the error k Ax b p k 1 . Numerical tests on a signal-processing-based example indicate that the proposed method is comparable to a method that parametrically minimizes the 1-norm of the solution x and the error k Ax b p k 1 , and that both methods are superior, by orders of magnitude, to solutions obtained by least squares as well by combinatorially choosing an optimal solution with a specific number of nonzero elements.

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"
    - "Massive Data Discrimination via Linear Support Vector Machines"

Connected through hyperedge HE-2 (induced by source paper "Mathematical Programming in Data Mining"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Feature Selection via Mathematical Programming"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Irrelevant Features and the Subset Selection Problem"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Mathematical Programming in Neural Networks"
    - "Individual and Collective Prognostic Prediction"

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