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: "a fast distributed proximal gradient method"
  Abstract: We present a distributed proximal-gradient method for optimizing the average of convex functions, each of which is the private local objective of an agent in a network with time-varying topology. The local objectives have distinct differentiable components, but they share a common nondifferentiable component, which has a favorable structure suitable for effective computation of the proximal operator. In our method, each agent iteratively updates its estimate of the global minimum by optimizing its local objective function, and exchanging estimates with others via communication in the network. Using Nesterov-type acceleration techniques and multiple communication steps per iteration, we show that this method converges at the rate 1/k (where k is the number of communication rounds between the agents), which is faster than the convergence rate of the existing distributed methods for solving this problem. The superior convergence rate of our method is also verified by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "distributed big data optimization via block communications"):
  with members:
    - "distributed big data optimization via block iterative convexification and averaging"
    - "a randomized primal distributed algorithm for partitioned and big data non convex optimization"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach"):
  with members:
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "estimation of sparse mimo channels with common support"
    - "fast distributed gradient methods"
    - "optimization with sparsity inducing penalties"
    - "distributed compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "distributed gradient methods with variable number of working nodes"):
  with members:
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "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"
    - "fast distributed gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (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-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 random deterministic parallel algorithms for nonconvex big data optimization"
    - "hybrid deterministic stochastic methods for data fitting"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "asynchronous distributed optimization via randomized dual proximal gradient"):
  with members:
    - "a polyhedral approximation framework for convex and robust distributed optimization"
    - "parallel selective algorithms for big data optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed abstract optimization via constraints consensus theory and applications"

Connected through hyperedge HE-7 (induced by source paper "asynchronous decentralized 20 questions for adaptive search"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "collaborative 20 questions for target localization"
    - "on decentralized estimation with active queries"
    - "active testing for face detection and localization"

Connected through hyperedge HE-8 (induced by source paper "a distributed algorithm for solving a linear algebraic equation"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "distributed strongly convex optimization"
    - "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)