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: "distributed deep learning using synchronous stochastic gradient descent"
  Abstract: We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a detailed analysis of scaling, and identify optimal design points for different networks. We demonstrate scaling of CNNs on 100s of nodes, and present what we believe to be record training throughputs. A 512 minibatch VGG-A CNN training run is scaled 90X on 128 nodes. Also 256 minibatch VGG-A and OverFeat-FAST networks are scaled 53X and 42X respectively on a 64 node cluster. We also demonstrate the generality of our approach via best-in-class 6.5X scaling for a 7-layer DNN on 16 nodes. Thereafter we attempt to democratize deep-learning by training on an Ethernet based AWS cluster and show ~14X scaling on 16 nodes.

Connected through hyperedge HE-1 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-2 (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"
    - "on scale out deep learning training for cloud and hpc"

Connected through hyperedge HE-3 (induced by source paper "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"):
  with members:
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"
    - "sparcml high performance sparse communication for machine learning"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-4 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "residual networks of residual networks multilevel residual networks"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "experience replay for continual learning"
    - "learning to teach"
    - "shakedrop regularization for deep residual learning"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "label refinery improving imagenet classification through label progression"

Connected through hyperedge HE-5 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "pyro deep universal probabilistic programming"
    - "an empirical model of large batch training"
    - "measuring the effects of data parallelism on neural network training"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "scaling sgd batch size to 32k for imagenet training"
    - "carla an open urban driving simulator"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"

Connected through hyperedge HE-6 (induced by source paper "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"):
  with members:
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "compiler level matrix multiplication optimization for deep learning"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "learning to optimize tensor programs"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "tuning and optimization for a variety of many core architectures without changing a single line of implementation code using the alpaka library"
    - "device placement optimization with reinforcement learning"
    - "searching for activation functions"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "optimal checkpointing for heterogeneous chains how to train deep neural networks with limited memory"):
  with members:
    - "divide and conquer checkpointing for arbitrary programs with no user annotation"
    - "the reversible residual network backpropagation without storing activations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "memory efficient implementation of densenets"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "identity mappings in deep residual networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"

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)