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: "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
  Abstract: A decision problem associated with a fundamental nonconvex model for linearly inseparable pattern sets is shown to be NP-complete. Another nonconvex model that employs an 1 norm instead of the 2-norm, can be solved in polynomial time by solving 2n linear programs, where n is the (usually small) dimensionality of the pattern space. An effective LP-based finite algorithm is proposed for solving the latter model. The algorithm is employed to obtain a noncon-vex piecewise-linear function for separating points representing measurements made on fine needle aspirates taken from benign and malignant human breasts. A computer program trained on 369 samples has correctly diagnosed each of 45 new samples encountered and is currently in use at the University of Wisconsin Hospitals. 1. Introduction. The fundamental problem we wish to address is that of

Connected through hyperedge HE-1 (induced by source paper "Induction of Oblique Decision Trees"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Learning When Reformulation is Appropriate for Iterative Design"
    - "Lookahead and Pathology in Decision Tree Induction"

Connected through hyperedge HE-2 (induced by source paper "Mathematical Programming in Neural Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Geometry in Learning"
    - "Feature Selection via Mathematical Programming"
    - "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"

Connected through hyperedge HE-3 (induced by source paper "Misclassification Minimization"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Induction of Oblique Decision Trees"

Connected through hyperedge HE-4 (induced by source paper "Geometry in Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "A System for Induction of Oblique Decision Trees"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Mathematical Programming in Neural Networks"

Connected through hyperedge HE-5 (induced by source paper "Bilinear Separation of Two Sets in n-Space"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mathematical Programming in Neural Networks"
    - "Geometry in Learning"
    - "Feature Selection via Mathematical Programming"

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