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: "weight standardization"
  Abstract: In this paper, we propose Weight Standardization (WS) to accelerate deep network training. WS is targeted at the micro-batch training setting where each GPU typically has only 1-2 images for training. The micro-batch training setting is hard because small batch sizes are not enough for training networks with Batch Normalization (BN), while other normalization methods that do not rely on batch knowledge still have difficulty matching the performances of BN in large-batch training. Our WS ends this problem because when used with Group Normalization and trained with 1 image/GPU, WS is able to match or outperform the performances of BN trained with large batch sizes with only 2 more lines of code. In micro-batch training, WS significantly outperforms other normalization methods. WS achieves these superior results by standardizing the weights in the convolutional layers, which we show is able to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients. The effectiveness of WS is verified on many tasks, including image classification, object detection, instance segmentation, video recognition, semantic segmentation, and point cloud recognition. The code is available here: https://github.com/joe-siyuan-qiao/WeightStandardization.

Connected through hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers"):
  with members:
    - "towards stabilizing batch statistics in backward propagation of batch normalization"
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a tensorized transformer for language modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "root mean square layer normalization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding and improving layer normalization"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks"):
  with members:
    - "a mean field theory of batch normalization"
    - "fixup initialization residual learning without normalization"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "a quantization friendly separable convolution for mobilenets"
    - "group normalization"
    - "spectrum concentration in deep residual learning a free probability approach"
    - "stable architectures for deep neural networks"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-3 (induced by source paper "getting to 99 accuracy in interactive segmentation"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "group normalization"
    - "interactive video object segmentation in the wild"
    - "iteratively trained interactive segmentation"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "iterative interaction training for segmentation editing networks"

Connected through hyperedge HE-4 (induced by source paper "f b alpha matting"):
  with members:
    - "natural image matting via guided contextual attention"
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "indices matter learning to index for deep image matting"
    - "context aware image matting for simultaneous foreground and alpha estimation"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "group normalization"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of video induced visual invariances"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "contrastive multiview coding"
    - "a systematic framework for natural perturbations from videos"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "self supervised learning via conditional motion propagation"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-6 (induced by source paper "large scale learning of general visual representations for transfer"):
  with members:
    - "self supervised learning of video induced visual invariances"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "billion scale semi supervised learning for image classification"
    - "momentum contrast for unsupervised visual representation learning"
    - "an exponential learning rate schedule for deep learning"
    - "do we train on test data purging cifar of near duplicates"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-8 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"
    - "image transformer"

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)