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: "scaling sgd batch size to 32k for imagenet training"
  Abstract: The most natural way to speed-up the training of large networks is to use data-parallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is to increase Learning Rate (LR) proportional to the batch size, and use special learning rate with "warm-up" policy to overcome initial optimization difficulty. #R##N#By controlling the LR during the training process, one can efficiently use large-batch in ImageNet training. For example, Batch-1024 for AlexNet and Batch-8192 for ResNet-50 are successful applications. However, for ImageNet-1k training, state-of-the-art AlexNet only scales the batch size to 1024 and ResNet50 only scales it to 8192. The reason is that we can not scale the learning rate to a large value. To enable large-batch training to general networks or datasets, we propose Layer-wise Adaptive Rate Scaling (LARS). LARS LR uses different LRs for different layers based on the norm of the weights and the norm of the gradients. By using LARS algoirithm, we can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet. Large batch can make full use of the system's computational power. For example, batch-4096 can achieve 3x speedup over batch-512 for ImageNet training by AlexNet model on a DGX-1 station (8 P100 GPUs).

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 "a simple framework for contrastive learning of visual representations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-3 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-4 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-5 (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"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "don t use large mini batches use local sgd"

Connected through hyperedge HE-6 (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-7 (induced by source paper "stochastic weight averaging in parallel large batch training that generalizes well"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "swalp stochastic weight averaging in low precision training"
    - "federated learning challenges methods and future directions"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "an empirical model of large batch training"
    - "don t use large mini batches use local sgd"
    - "averaging weights leads to wider optima and better generalization"

Connected through hyperedge HE-8 (induced by source paper "on the distance between two neural networks and the stability of learning"):
  with members:
    - "optimization for deep learning theory and algorithms"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "huggingface s transformers state of the art natural language processing"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "measuring and regularizing networks in function space"

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)