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: "multi gpu training of convnets"
  Abstract: In this work we evaluate different approaches to parallelize computation of convolutional neural networks across several GPUs.

Connected through hyperedge HE-1 (induced by source paper "large batch training does not need warmup"):
  with members:
    - "large batch training for lstm and beyond"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "on lipschitz bounds of general convolutional neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "image classification at supercomputer scale"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-4 (induced by source paper "ouroboros on accelerating training of transformer based language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "decoupled parallel backpropagation with convergence guarantee"
    - "training neural networks using features replay"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-5 (induced by source paper "chaos a parallelization scheme for training convolutional neural networks on intel xeon phi"):
  with members:
    - "comparative study of deep learning software frameworks"
    - "benchmarking state of the art deep learning software tools"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "one weird trick for parallelizing convolutional neural networks"
    - "comparative performance analysis of intel xeon phi gpu and cpu"
    - "optimizing the mapreduce framework on intel xeon phi coprocessor"

Connected through hyperedge HE-6 (induced by source paper "efficient super resolution for large scale images using attentional gan"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "is the deconvolution layer the same as a convolutional layer"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "decoupled parallel backpropagation with convergence guarantee"):
  with members:
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "benefits of depth in neural networks"
    - "densely connected convolutional networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "exploiting feature and class relationships in video categorization with regularized deep neural networks"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "going deeper with convolutions"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "using web co occurrence statistics for improving image categorization"

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.LG(Machine Learning)