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 "scalable distributed dnn training using tensorflow and cuda aware mpi characterization designs and performance evaluation"):
  Abstract: The current wave of advances in Deep Learning (DL) have been triggered by the availability of large-scale datasets, efficient CPU and GPU hardware, and development of software frameworks like TensorFlow (TF). However, little exists in literature that addresses TensorFlow's distributed training capabilities. In this paper, we provide an in-depth performance characterization and design analysis for distributed TensorFlow. We present three key insights: 1) Horovod designs achieve better performance compared to the official gRPC-based approaches, 2) performance of Horovod design is heavily influenced by the time spent in gradient aggregation that uses the Allreduce primitive, and 3) performance of existing Horovod-MPI implementation is significantly worse compared to Horovod-NCCL. To address this limitation in Horovod-MPI, we propose a novel and efficient CUDA-Aware MPI Allreduce design that 1) exploits CUDA kernels to perform large reductions on the GPU, 2) uses a com-bination of bandwidth-optimal and latency-optimal algorithms, and 3) maintains a pointer cache to avoid CUDA-driver query overheads in the critical path. The proposed designs deliver 5×, 17×, and 29% better performance compared to NCCL2 for small, medium, and large messages. Our designs enable Horovod-MPI to beat state-of-the-art Horovod-NCCL2 by 3% and achieve 90% scaling efficiency for ResNet-50 training on 64 Pascal GPUs.

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 "on scale out deep learning training for cloud and hpc":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training")
  Member "horovod fast and easy distributed deep learning in tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  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 "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "performance modeling and evaluation of distributed deep learning frameworks on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)