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 admm for large scale optimization part ii linear convergence analysis and numerical performance"
  Abstract: The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems efficiently. When the data size and/or the problem dimension is large, a distributed version of ADMM can be used, which is capable of distributing the computation load and the data set to a network of computing nodes. Unfortunately, a direct synchronous implementation of such algorithm does not scale well with the problem size, as the algorithm speed is limited by the slowest computing nodes. To address this issue, in a companion paper, we have proposed an asynchronous distributed ADMM (AD-ADMM) and studied its worst-case convergence conditions. In this paper, we further the study by characterizing the conditions under which the AD-ADMM achieves linear convergence. Our conditions as well as the resulting linear rates reveal the impact that various algorithm parameters, network delay, and network size have on the algorithm performance. To demonstrate the superior time efficiency of the proposed AD-ADMM, we test the AD-ADMM on a high-performance computer cluster by solving a large-scale logistic regression problem.

Connected through hyperedge HE-1 (induced by source paper "harnessing the power of serverless runtimes for large scale optimization"):
  with members:
    - "characterizing task completion latencies in fog computing"
    - "numpywren serverless linear algebra"
    - "serving deep learning models in a serverless platform"
    - "serverless computing current trends and open problems"
    - "hemingway modeling distributed optimization algorithms"
    - "revisiting distributed synchronous sgd"
    - "a sequential approximation framework for coded distributed optimization"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"

Connected through hyperedge HE-2 (induced by source paper "distributed second order methods with variable number of working nodes"):
  with members:
    - "distributed gradient methods with variable number of working nodes"
    - "newton like method with diagonal correction for distributed optimization"
    - "hybrid deterministic stochastic methods for data fitting"

Connected through hyperedge HE-3 (induced by source paper "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "a distributed asynchronous and incremental algorithm for nonconvex optimization an admm based approach"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"

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)