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 "redsync reducing synchronization bandwidth for distributed deep learning training system"):
  Abstract: Abstract   Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes. Since the bandwidth requirement of synchronizing the gradients of the local model can be a bottleneck for large-scale distributed training, compressing communication traffic has gained widespread attention recently. Among several recent proposed compression algorithms, Residual Gradient Compression (RGC) is one of the most successful approaches—it can significantly compress the transmitting message size (0.1% of the gradient size) of each node and still achieve correct accuracy and the same convergence speed. However, the literature on compressing deep networks focuses almost exclusively on achieving good theoretical compression rate, while the efficiency of RGC in real implementation has been less investigated. In this paper, we develop an RGC method that is able to reduce the end-to-end training time on real-world multi-GPU systems. Our proposed RGC system design called RedSync, introduces a set of optimizations to reduce communication bandwidth while introducing limited overhead. We examine the performance of RedSync on two different multiple GPU platforms, including 128 GPUs of a supercomputer and an 8-GPU server. Our test cases include image classification on Cifar10 and ImageNet, and language modeling tasks on Penn Treebank and Wiki2 datasets. For DNNs featured with high communication to computation ratio, which has long been considered with poor scalability, RedSync shows significant performance improvement.

Includes members:
  Member "sparcml high performance sparse communication for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching for winograd aware quantized networks")
  Member "sparse binary compression towards distributed deep learning with minimal communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "imagenet training in minutes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "deep gradient compression reducing the communication bandwidth for distributed training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "adacomp adaptive residual gradient compression for data parallel distributed training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "sparse communication for distributed gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "terngrad ternary gradients to reduce communication in distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "communication optimization strategies for distributed deep learning a survey")

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)