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: "Parallel Gradient Distribution in Unconstrained Optimization"
  Abstract: A parallel version is proposed for a fundamental theorem of serial unconstrained optimization. The parallel theorem allows each of k parallel processors to use simultaneously a different algorithm, such as a descent, Newton, quasi-Newton or a conjugate gradient algorithm. Each processor can perform one or many steps of a serial algorithm on a portion of the gradient of the objective function assigned to it, independently of the other processors. Eventually a synchronization step is performed which, for differentiable convex functions, consists of taking a strong convex combination of the k points found by the k processors. For nonconvex, as well as convex, differentiable functions, the best point found by the k processors is taken, or any better point. The fundamental result that we establish is that any accumulation point of the parallel algorithm is stationary for the nonconvex case, and is a global solution for the convex case. Computational testing on the Thinking Machines CM-5 multiprocessor indicate a speedup of the order of the number of processors employed.

Connected through hyperedge HE-1 (induced by source paper "Serial and Parallel Multicategory Discrimination"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Mathematical Programming in Neural Networks"
    - "June 1994 T o app ear in Neural Computation A Coun terexample to T emp"

Connected through hyperedge HE-3 (induced by source paper "New Inexact Parallel Variable Distribution Algorithms  Editor:"):
  with members:
    - "ERROR STABILITY PROPERTIES OF GENERALIZED GRADIENT-TYPE ALGORITHMS"

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