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 i algorithm and convergence analysis"
  Abstract: Aiming at solving large-scale optimization problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the optimization problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. This is particularly true in a heterogeneous network where the computing nodes experience different computation and communication delays. In this paper, we propose an asynchronous distributed ADMM (AD-ADMM), which can effectively improve the time efficiency of distributed optimization. Our main interest lies in analyzing the convergence conditions of the AD-ADMM, under the popular partially asynchronous model, which is defined based on a maximum tolerable delay of the network. Specifically, by considering general and possibly non-convex cost functions, we show that the AD-ADMM is guaranteed to converge to the set of Karush–Kuhn–Tucker (KKT) points as long as the algorithm parameters are chosen appropriately according to the network delay. We further illustrate that the asynchrony of the ADMM has to be handled with care, as slightly modifying the implementation of the AD-ADMM can jeopardize the algorithm convergence, even under the standard convex setting.

Connected through hyperedge HE-1 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "probabilistic synchronous parallel"
    - "revisiting distributed synchronous sgd"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "web scale topic models in spark an asynchronous parameter server"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-2 (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 ii linear convergence analysis and numerical performance"

Connected through hyperedge HE-3 (induced by source paper "decentralized consensus optimization based on parallel random walk"):
  with members:
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"

Connected through hyperedge HE-4 (induced by source paper "multi path alpha fair resource allocation at scale in distributed software defined networks"):
  with members:
    - "lower bounds for the fair resource allocation problem"
    - "real time fair resource allocation in distributed software defined networks"
    - "a fast distributed stateless algorithm for alpha fair packing problems"
    - "software defined networking a comprehensive survey"
    - "disco distributed multi domain sdn controllers"
    - "optimization with sparsity inducing penalties"
    - "congestion control with explicit rate indication"

Connected through hyperedge HE-5 (induced by source paper "distributed optimization using the primal dual method of multipliers"):
  with members:
    - "analysis of sum weight like algorithms for averaging in wireless sensor networks"
    - "online alternating direction method"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "impact of communication delay on asynchronous distributed optimal power flow using admm"):
  with members:
    - "distributed algorithms for peak ramp minimization problem in smart grid"

Connected through hyperedge HE-7 (induced by source paper "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "linear convergence rate of a class of distributed augmented lagrangian algorithms"

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)