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: "Serial and Parallel Multicategory Discrimination"
  Abstract: A parallel algorithm is proposed for a fundamental problem of machine learning, that of mul-ticategory discrimination. The algorithm is based on minimizing an error function associated with a set of highly structured linear inequalities. These inequalities characterize piecewise-linear separation of k sets by the maximum of k affine functions. The error function has a Lipschitz continuous gradient that allows the use of fast serial and parallel unconstrained minimization algorithms. A serial quasi-Newton algorithm is considerably faster than previous linear programming formulations. A parallel gradient distribution algorithm is used to parallelize the error-minimization problem. Preliminary computational results are given for both a DECstation

Connected through hyperedge HE-1 (induced by source paper "Parallel Gradient Distribution in Unconstrained Optimization"):
  with members:
    - "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"
    - "New Inexact Parallel Variable Distribution Algorithms  Editor:"

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