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 "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  Abstract: Due to the big size of data and limited data storage volume of a single computer or a signal server, data are often stored in a distributed manner. Thus, performing large-scale machine learning operations with the distributed datasets through communication networks is often required. In this paper, we investigate the impact of network communication constraints on the convergence speed of the communication-efficient distributed machine learning algorithm. Firstly, we study the convergence rate of the distributed dual coordinate ascent algorithm in a general tree structured network. Since a tree network model can be understood as the generalization of a star network model, our algorithm can be thought of as the generalization of the distributed dual coordinate ascent in a star network model. Secondly, by considering network communication delays, we optimize the network-constrained distributed dual coordinate ascent algorithm to maximize its convergence speed. In numerical experiments, we take into account federated learning scenarios, where local workers cannot directly reach to a central node due to communication constraints. Through extensive numerical experiments, we demonstrate that the usability of our distributed dual coordinate ascent algorithm in a tree network in the federated learning scenarios. Additionally, under different network communication delays, the delay-dependent number of local and global iterations in the distributed dual coordinated ascent can improve the convergence speed.

Includes members:
  Member "distributed federated learning for ultra reliable low latency vehicular communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "energy efficient federated learning over wireless communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "supervised learning under distributed features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure distributed on device learning networks with byzantine adversaries")
  Member "a joint learning and communications framework for federated learning over wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "communication efficient distributed dual coordinate ascent for machine learning in general tree networks")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "fast distributed asynchronous sgd with variance reduction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block stochastic gradient descent for large scale tomographic reconstruction in a parallel network")

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)