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: "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
  Abstract: Consider a set of networked agents endowed with private cost functions and seeking to find a consensus on the minimizer of the aggregate cost. A new class of random asynchronous distributed optimization methods is introduced. The methods generalize the standard Alternating Direction Method of Multipliers (ADMM) to an asynchronous setting where isolated components of the network are activated in an uncoordinated fashion. The algorithms rely on the introduction of randomized Gauss-Seidel iterations of a Douglas-Rachford operator for finding zeros of a sum of two monotone operators. Convergence to the sought minimizers is provided under mild connectivity conditions. Numerical results sustain our claims.

Connected through hyperedge HE-1 (induced by source paper "popsgd decentralized stochastic gradient descent in the population model"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "decentralization meets quantization"
    - "stochastic gradient push for distributed deep learning"
    - "scaling sgd batch size to 32k for imagenet training"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "decentralized stochastic optimization and gossip algorithms with compressed communication"):
  with members:
    - "communication compression for decentralized training"
    - "stochastic gradient push for distributed deep learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "fast distributed gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-3 (induced by source paper "asynchronous gradient push"):
  with members:
    - "asynchronous subgradient push"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "asynchronous subgradient push"):
  with members:
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-5 (induced by source paper "filtering random graph processes over random time varying graphs"):
  with members:
    - "autoregressive moving average graph filtering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "2 dimensional finite impulse response graph temporal filters"
    - "distributed autoregressive moving average graph filters"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-6 (induced by source paper "a survey of stochastic simulation and optimization methods in signal processing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "communication efficient distributed dual coordinate ascent"
    - "on the convergence of approximate message passing with arbitrary matrices"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (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"
    - "parallel selective algorithms for big data optimization"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "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"

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)