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: "ERROR STABILITY PROPERTIES OF GENERALIZED GRADIENT-TYPE ALGORITHMS"
  Abstract: We present a unified framework for convergence analysis of the generalized subgradient-type algorithms in the presence of perturbations. One of the principal novel features of our analysis is that perturbations need not tend to zero in the limit. It is established that the iterates of the algorithms are attracted, in a certain sense, to an "-stationary set of the problem, where " depends on the magnitude of perturbations. Characterization of those attraction sets is given in the general (nonsmooth and nonconvex) case. The results are further strengthened for convex, weakly sharp and strongly convex problems. Our analysis extends and unifies previously known results on convergence and stability properties of gradient and subgradient methods, including their incremental, parallel and "heavy ball" modifications. fl The first author is supported in part by CNPq grant 300734/95-6. Research of the second author was supported in part by the International Science Foundation Grant NBY000, the International Science Foundation and Russian Goverment Grant NBY300 and the Russian Foundation for Fundamental Research Grant N 95-01-01448. y Instituto de Matematica Pura e Aplicada, Estrada Dona Castorina 110, Jardim Bot^anico, Rio de Janeiro, RJ, CEP 22460-320, Brazil. Email : solodov@impa.br. z Operations Research Department, Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia, 119899.

Connected through hyperedge HE-1 (induced by source paper "New Inexact Parallel Variable Distribution Algorithms  Editor:"):
  with members:
    - "Parallel Gradient Distribution in Unconstrained Optimization"

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