human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "communication efficient distributed dual coordinate ascent for machine learning in general tree networks"):
  Abstract: Due to the size of data and the limited data storage space in a single local computer, data can often be stored in a distributed manner. In order to use the distributed big data in machine learning, performing large-scale machine learning from the distributed data through communication networks is inevitable. In this paper, we investigate the impact of network communication constraints on the convergence speed of distributed machine learning optimization algorithms. Firstly, we study the convergence rate of the distributed dual coordinate ascent in a general tree structured network, since every connected communication network can have a spanning tree, and a tree network can be understood as the generalization of a star network. Secondly, by considering network communication delays, we optimize the network-constrained dual coordinate ascent to maximize its convergence speed in terms of operation time. Through numerical experiments, we demonstrate that under different network communication delays, the delay-dependent number of local and global iterations in distributed dual coordinated ascent can play a significant role in the achievement of maximum convergence speed.

Includes members:
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "distributed asynchronous dual free stochastic dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "passcode parallel asynchronous stochastic dual co ordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning")

Question: Please tell me which class the source paper 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)