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 "auto precision scaling for distributed deep learning"):
  Abstract: In recent years, large-batch optimization is becoming the key of distributed deep learning. However, large-batch optimization is hard. Straightforwardly porting the code often leads to a significant loss in testing accuracy. As some researchers suggested that large batch optimization leads to a low generalization performance, and they further conjectured that large-batch training needs a higher floating-point precision to achieve a higher generalization performance. To solve this problem, we conduct an open study in this paper. Our target is to find the number of bits that large-batch training needs. To do so, we need a system for customized precision study. However, state-of-the-art systems have some limitations that lower the efficiency of developers and researchers. To solve this problem, we design and implement our own system CPD: A High Performance System for Customized-Precision Distributed DL. In our experiments, our application often loses accuracy if we use a very-low precision (e.g. 8 bits or 4 bits). To solve this problem, we proposed the APS (Auto-Precision-Scaling) algorithm, which is a layer-wise adaptive scheme for gradients shifting. With APS, we are able to make the large-batch training converge with only 4 bits.

Includes members:
  Member "rethinking floating point for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "image classification at supercomputer scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "training deep neural networks with 8 bit floating point numbers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  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 "scaling sgd batch size to 32k for imagenet training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "virtualizing deep neural networks for memory efficient neural network design":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling back propagation by parallel scan algorithm")
  Member "training deep nets with sublinear memory cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching for winograd aware quantized 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)