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: "parallel selective algorithms for big data optimization"
  Abstract: We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss- Seidel (i.e., sequential) ones, as well as virtually all possibilities "in between" with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results on LASSO, logistic regression, and some nonconvex quadratic problems show that the new method consistently outperforms existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "a randomized primal distributed algorithm for partitioned and big data non convex optimization"):
  with members:
    - "next in network nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "parallel and distributed methods for nonconvex optimization part ii applications"):
  with members:
    - "parallel and distributed methods for nonconvex optimization part i theory"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"
    - "alternating rate profile optimization in single stream mimo interference channels"
    - "coordinated multicast beamforming in multicell networks"
    - "distributed optimal beamformers for cognitive radios robust to channel uncertainties"
    - "sum rate maximization in two way af mimo relaying polynomial time solutions to a class of dc programming problems"
    - "achieving global optimality for weighted sum rate maximization in the k user gaussian interference channel with multiple antennas"

Connected through hyperedge HE-3 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex 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 proximal dual consensus admm method for multi agent constrained optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "coordinated home energy management for real time power balancing"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-5 (induced by source paper "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"):
  with members:
    - "hybrid random deterministic parallel algorithms for nonconvex 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-6 (induced by source paper "distributed nonconvex multiagent optimization over time varying networks"):
  with members:
    - "next in network nonconvex optimization"
    - "decomposition by partial linearization parallel optimization of multi agent systems"

Connected through hyperedge HE-7 (induced by source paper "doubly random parallel stochastic methods for large scale learning"):
  with members:
    - "res regularized stochastic bfgs algorithm"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "asynchronous distributed optimization via randomized dual proximal gradient"):
  with members:
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "a fast distributed proximal gradient method"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed abstract optimization via constraints consensus theory and applications"

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)