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: "New Inexact Parallel Variable Distribution Algorithms  Editor:"
  Abstract: We consider the recently proposed parallel variable distribution (PVD) algorithm of Ferris and Mangasarian [4] for solving optimization problems in which the variables are distributed among p processors. Each processor has the primary responsibility for updating its block of variables while allowing the remaining "secondary" variables to change in a restricted fashion along some easily computable directions. We propose useful generalizations that consist, for the general unconstrained case, of replacing exact global solution of the subproblems by a certain natural sufficient descent condition, and, for the convex case, of inexact subproblem solution in the PVD algorithm. These modifications are the key features of the algorithm that has not been analyzed before. The proposed modified algorithms are more practical and make it easier to achieve good load balancing among the parallel processors. We present a general framework for the analysis of this class of algorithms and derive some new and improved linear convergence results for problems with weak sharp minima of order 2 and strongly convex problems. We also show that nonmonotone synchronization schemes are admissible, which further improves flexibility of PVD approach.

Connected through hyperedge HE-1 (induced by source paper "Parallel Gradient Distribution in Unconstrained Optimization"):
  with members:
    - "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"
    - "Serial and Parallel Multicategory Discrimination"

Connected through hyperedge HE-2 (induced by source paper "ERROR STABILITY PROPERTIES OF GENERALIZED GRADIENT-TYPE ALGORITHMS"):
  with members:
    (none)

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