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: "cutmix regularization strategy to train strong classifiers with localizable features"
  Abstract: Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout remove informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it leads to information loss and inefficiency during training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on the ImageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances. Source code and pretrained models are available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"
    - "using self supervised learning can improve model robustness and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "dropblock a regularization method for convolutional networks"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial logit pairing"
    - "autoaugment learning augmentation policies from data"
    - "shakedrop regularization for deep residual learning"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "understanding and enhancing mixed sample data augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "natural adversarial examples"
    - "fast autoaugment"
    - "data augmentation revisited rethinking the distribution gap between clean and augmented data"
    - "human uncertainty makes classification more robust"
    - "data augmentation by pairing samples for images classification"
    - "improved mixed example data augmentation"

Connected through hyperedge HE-4 (induced by source paper "supermix supervising the mixing data augmentation"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "variational information distillation for knowledge transfer"
    - "fast autoaugment"
    - "contrastive representation distillation"
    - "smoothfool an efficient framework for computing smooth adversarial perturbations"

Connected through hyperedge HE-5 (induced by source paper "adversarial based knowledge distillation for multi model ensemble and noisy data refinement"):
  with members:
    - "relational knowledge distillation"
    - "towards instance level image to image translation"
    - "snapshot distillation teacher student optimization in one generation"
    - "knowledge distillation by on the fly native ensemble"
    - "multimodal unsupervised image to image translation"
    - "large scale distributed neural network training through online distillation"
    - "meal multi model ensemble via adversarial learning"
    - "learning to reweight examples for robust deep 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"
    - "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"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"

Connected through hyperedge HE-7 (induced by source paper "achieving robustness in the wild via adversarial mixing with disentangled representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "do imagenet classifiers generalize to imagenet"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"

Connected through hyperedge HE-8 (induced by source paper "fixing the train test resolution discrepancy"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "the herbarium challenge 2019 dataset"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

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)