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: "explicit convergence rate of a distributed alternating direction method of multipliers"
  Abstract: Consider a set of N agents seeking to solve distributively the minimization problem $\inf_{x} \sum_{n = 1}^N f_n(x)$ where the convex functions $f_n$ are local to the agents. The popular Alternating Direction Method of Multipliers has the potential to handle distributed optimization problems of this kind. We provide a general reformulation of the problem and obtain a class of distributed algorithms which encompass various network architectures. The rate of convergence of our method is considered. It is assumed that the infimum of the problem is reached at a point $x_\star$, the functions $f_n$ are twice differentiable at this point and $\sum \nabla^2 f_n(x_\star) > 0$ in the positive definite ordering of symmetric matrices. With these assumptions, it is shown that the convergence to the consensus $x_\star$ is linear and the exact rate is provided. Application examples where this rate can be optimized with respect to the ADMM free parameter $\rho$ are also given.

Connected through hyperedge HE-1 (induced by source paper "networked parallel algorithms for robust convex optimization via the scenario approach"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "robust regression and lasso"

Connected through hyperedge HE-2 (induced by source paper "distributed algorithms for computation of centrality measures in complex networks"):
  with members:
    - "stability and continuity of centrality measures in weighted graphs"
    - "ergodic randomized algorithms and dynamics over networks"
    - "convergence of distributed randomized pagerank algorithms"
    - "axioms for centrality"
    - "distributed randomized algorithms for the pagerank computation"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"

Connected through hyperedge HE-3 (induced by source paper "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"):
  with members:
    - "networked parallel algorithms for robust convex optimization via the scenario approach"
    - "distributed random fixed projected algorithm for constrained optimization over digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "average consensus on general strongly connected digraphs"

Connected through hyperedge HE-4 (induced by source paper "cooperative localization in wsns a hybrid convex non convex solution"):
  with members:
    - "distributed maximum likelihood sensor network localization"

Connected through hyperedge HE-5 (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"
    - "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)