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: "massively distributed sgd imagenet resnet 50 training in a flash"
  Abstract: Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "limitations of the empirical fisher approximation"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "information matrices and generalization"
    - "image classification at supercomputer scale"
    - "don t use large mini batches use local sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-3 (induced by source paper "large batch training does not need warmup"):
  with members:
    - "large batch training for lstm and beyond"
    - "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"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-4 (induced by source paper "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-6 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "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"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-7 (induced by source paper "reducing bert pre training time from 3 days to 76 minutes"):
  with members:
    - "large batch training for lstm and beyond"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"

Connected through hyperedge HE-8 (induced by source paper "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"):
  with members:
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "tictac accelerating distributed deep learning with communication scheduling"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "don t decay the learning rate increase the batch size"

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)