human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on scale out deep learning training for cloud and hpc"
  Abstract: The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes. This can only be achieved through scalable and efficient distributed training, since a single node/card cannot satisfy the compute, memory, and I/O requirements of today's state-of-the-art deep neural networks. However, scaling synchronous Stochastic Gradient Descent (SGD) is still a challenging problem and requires continued research/development. This entails innovations spanning algorithms, frameworks, communication libraries, and system design. In this paper, we describe the philosophy, design, and implementation of Intel Machine Learning Scalability Library (MLSL) and present proof-points demonstrating scaling DL training on 100s to 1000s of nodes across Cloud and HPC systems.

Connected through hyperedge HE-1 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training"):
  with members:
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "scaling neural machine translation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "three factors influencing minima in sgd"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-2 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning"):
  with members:
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "a read write memory network for movie story understanding"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "deep learning for video classification and captioning"

Connected through hyperedge HE-4 (induced by source paper "scalable distributed dnn training using tensorflow and cuda aware mpi characterization designs and performance evaluation"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "learning transferable architectures for scalable image recognition"
    - "scaling sgd batch size to 32k for imagenet training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "benchmarking state of the art deep learning software tools"

Connected through hyperedge HE-5 (induced by source paper "communication scheduling as a first class citizen in distributed machine learning systems"):
  with members:
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "identity mappings in deep residual networks"
    - "tensorflow a system for large scale machine learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "anatomy of high performance deep learning convolutions on simd architectures"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "mixed precision training"
    - "low memory gemm based convolution algorithms for deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "parallel multi channel convolution using general matrix multiplication"
    - "distributed deep learning using synchronous stochastic gradient descent"

Question: Please tell me which class the center node 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)