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: "theano mpi a theano based distributed training framework"
  Abstract: We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelism. Both synchronous and asynchronous training are implemented in our framework, where parameter exchange among GPUs is based on CUDA-aware MPI. In this report, we analyze the convergence and capability of the framework to reduce training time when scaling the synchronous training of AlexNet and GoogLeNet from 2 GPUs to 8 GPUs. In addition, we explore novel ways to reduce the communication overhead caused by exchanging parameters. Finally, we release the framework as open-source for further research on distributed deep learning

Connected through hyperedge HE-1 (induced by source paper "efficient and robust parallel dnn training through model parallelism on multi gpu platform"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "beyond data and model parallelism for deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "characterizing deep learning i o workloads in tensorflow"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "exploring scientific application performance using large scale object storage"
    - "nvidia tensor core programmability performance precision"
    - "the sage project a storage centric approach for exascale computing"
    - "in datacenter performance analysis of a tensor processing unit"
    - "benchmarking state of the art deep learning software tools"
    - "exploring application performance on emerging hybrid memory supercomputers"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "empirical evaluation of parallel training algorithms on acoustic modeling"):
  with members:
    - "asynchronous stochastic gradient descent with delay compensation"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "experiments on parallel training of deep neural network using model averaging"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "gpu asynchronous stochastic gradient descent to speed up neural network training"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial parallelization"):
  with members:
    - "generating images with recurrent adversarial networks"
    - "improved techniques for training gans"
    - "on the quantitative analysis of decoder based generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-5 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"
    - "boda rtc productive generation of portable efficient code for convolutional neural networks on mobile computing platforms"

Connected through hyperedge HE-6 (induced by source paper "distributed training of deep neural networks theoretical and practical limits of parallel scalability"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep learning with limited numerical precision"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)