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 "speeding up deep learning with transient servers"):
  Abstract: Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such speedups are often desirable---e.g., for rapidly evaluating new model designs---they often come with significantly higher monetary costs due to sublinear scalability. In this paper, we investigate the feasibility of using training clusters composed of cheaper transient GPU servers to get the benefits of distributed training without the high costs. #R##N#We conduct the first large-scale empirical analysis, launching more than a thousand GPU servers of various capacities, aimed at understanding the characteristics of transient GPU servers and their impact on distributed training performance. Our study demonstrates the potential of transient servers with a speedup of 7.7X with more than 62.9% monetary savings for some cluster configurations. We also identify a number of important challenges and opportunities for redesigning distributed training frameworks to be transient-aware. For example, the dynamic cost and availability characteristics of transient servers suggest the need for frameworks to dynamically change cluster configurations to best take advantage of current conditions.

Includes members:
  Member "demystifying parallel and distributed deep learning an in depth concurrency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "sparse weight activation training")
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching for winograd aware quantized networks")
  Member "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "performance modeling and evaluation of distributed deep learning frameworks on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs")
  Member "portfolio driven resource management for transient cloud servers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cloudcoaster transient aware bursty datacenterworkload scheduling")
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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.PF(Performance)