human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
  Abstract: We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. The main contribution of this work is a novel \emph{parallel, hybrid random/deterministic} decomposition scheme wherein, at each iteration, a subset of (block) variables is updated at the same time by minimizing local convex approximations of the original nonconvex function. To tackle with huge-scale problems, the (block) variables to be updated are chosen according to a \emph{mixed random and deterministic} procedure, which captures the advantages of both pure deterministic and random update-based schemes. Almost sure convergence of the proposed scheme is established. Numerical results show that on huge-scale problems the proposed hybrid random/deterministic algorithm outperforms both random and deterministic schemes.

Connected through hyperedge HE-1 (induced by source paper "distributed partitioned big data optimization via asynchronous dual decomposition"):
  with members:
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "multi agent distributed optimization via inexact consensus admm"
    - "a polyhedral approximation framework for convex and robust distributed optimization"

Connected through hyperedge HE-2 (induced by source paper "distributed gradient methods with variable number of working nodes"):
  with members:
    - "on the influence of informed agents on learning and adaptation over networks"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "hybrid deterministic stochastic methods for data fitting"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "parallel selective algorithms for big data optimization"
    - "distributed detection and estimation in wireless sensor networks"
    - "distributed compressed sensing for static and time varying networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "distributed maximum likelihood sensor network localization"
    - "sparse distributed learning based on diffusion adaptation"

Connected through hyperedge HE-4 (induced by source paper "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"):
  with members:
    - "parallel selective algorithms for big data optimization"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "real and complex monotone communication games"
    - "averaged iterative water filling algorithm robustness and convergence"

Connected through hyperedge HE-5 (induced by source paper "newton like method with diagonal correction for distributed optimization"):
  with members:
    - "distributed gradient methods with variable number of working nodes"
    - "hybrid deterministic stochastic methods for data fitting"
    - "a fast distributed proximal gradient method"
    - "finite time and asymptotic convergence of distributed averaging and maximizing algorithms"
    - "fast distributed gradient methods"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DC(Distributed, Parallel, and Cluster Computing)