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: "imagenet training in minutes"
  Abstract: Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10^18 single precision operations in total. On the other hand, the world's current fastest supercomputer can finish 2 * 10^17 single precision operations per second (Dongarra et al 2017, this https URL). If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in one minute. However, the current bottleneck for fast DNN training is in the algorithm level. Specifically, the current batch size (e.g. 512) is too small to make efficient use of many processors. For large-scale DNN training, we focus on using large-batch data-parallelism synchronous SGD without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman, Ginsburg, 2017, arXiv:1708.03888) enables us to scale the batch size to extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with AlexNet in 11 minutes on 1024 CPUs. About three times faster than Facebook's result (Goyal et al 2017, arXiv:1706.02677), we finish the 90-epoch ImageNet training with ResNet-50 in 20 minutes on 2048 KNLs without losing accuracy. State-of-the-art ImageNet training speed with ResNet-50 is 74.9% top-1 test accuracy in 15 minutes. We got 74.9% top-1 test accuracy in 64 epochs, which only needs 14 minutes. Furthermore, when we increase the batch size to above 16K, our accuracy is much higher than Facebook's on corresponding batch sizes. Our source code is available upon request.

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 "chameleon adaptive code optimization for expedited deep neural network compilation"):
  with members:
    - "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning scheduling algorithms for data processing clusters"

Connected through hyperedge HE-3 (induced by source paper "a new mram based process in memory accelerator for efficient neural network training with floating point precision"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "critical learning periods in deep neural networks"
    - "computing in memory with spin transfer torque magnetic ram"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "do optimization methods in deep learning applications matter"):
  with members:
    - "a survey of optimization methods from a machine learning perspective"
    - "quasi newton optimization methods for deep learning applications"
    - "deep reinforcement learning via l bfgs optimization"
    - "is asgd accelerating asynchronous sgd using importance sampling"
    - "deep learning with elastic averaging sgd"

Connected through hyperedge HE-5 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-6 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "adascale towards real time video object detection using adaptive scaling"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-7 (induced by source paper "efficient contextual representation learning without softmax layer"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "multilingual constituency parsing with self attention and pre training"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-8 (induced by source paper "prunetrain fast neural network training by dynamic sparse model reconfiguration"):
  with members:
    - "mini batch serialization cnn training with inter layer data reuse"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "amc automl for model compression and acceleration on mobile devices"
    - "focal loss for dense object detection"
    - "don t decay the learning rate increase the batch size"
    - "learning intrinsic sparse structures within long short term memory"
    - "squeeze and excitation networks"
    - "mixed precision training"

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.CV(Computer Vision and Pattern Recognition)