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 optimization strategies for distributed deep learning a survey"):
  Abstract: Recent trends in high-performance computing and deep learning lead to a proliferation of studies on large-scale deep neural network (DNN) training. However, the frequent communication requirements among computation nodes drastically slow down the overall training speed, which makes the bottleneck in distributed training, particularly in clusters with limited network bandwidth. To mitigate the drawbacks of distributed communication, researchers have proposed various optimization strategies. In this paper, we give a comprehensive survey of communication strategies from both algorithm and computer network perspectives. Algorithm optimizations focus on reducing the amount of communication in distributed training, while network optimizations focus on speeding up the communication between distributed devices. At the algorithm level, we describe how to reduce the number of communication rounds and transmitted bits per round, besides we shed light on how to overlap computation and communication. At the network level, we discuss the effect caused by network infrastructures, including communication schemes, network protocols, and topology. Finally, we extrapolate potential challenges and research directions for communication acceleration in distributed DNN training.

Includes members:
  Member "error feedback fixes signsgd and other gradient compression schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "powersgd practical low rank gradient compression for distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "3lc lightweight and effective traffic compression for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "priority based parameter propagation for distributed dnn training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic parameter allocation in parameter servers")
  Member "large batch training for lstm and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large batch training does not need warmup")
  Member "scalable deep learning on distributed infrastructures challenges techniques and tools":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "characterizing deep learning training workloads on alibaba pai")
  Member "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the future of digital health with federated learning")
  Member "redsync reducing synchronization bandwidth for distributed deep learning training system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks")

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)