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: "adversarial examples improve image recognition"
  Abstract: Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. #R##N#We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger. For instance, by applying AdvProp to the latest EfficientNet-B7 [28] on ImageNet, we achieve significant improvements on ImageNet (+0.7%), ImageNet-C (+6.5%), ImageNet-A (+7.0%), Stylized-ImageNet (+4.8%). With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images (~3000X more than ImageNet) and ~9.4X more parameters. Models are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation"):
  with members:
    - "convolutional networks with dense connectivity"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "implicit semantic data augmentation for deep networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bertscore evaluating text generation with bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "natural adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-3 (induced by source paper "automatic shortcut removal for self supervised representation learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "self supervised learning of video induced visual invariances"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-4 (induced by source paper "compounding the performance improvements of assembled techniques in a convolutional neural network"):
  with members:
    - "selective kernel networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "dropblock a regularization method for convolutional networks"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-5 (induced by source paper "maxup a simple way to improve generalization of neural network training"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "improving neural language modeling via adversarial training"
    - "theoretically principled trade off between robustness and accuracy"
    - "freelb enhanced adversarial training for language understanding"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "do cnns encode data augmentations"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "regularized evolution for image classifier architecture search"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-7 (induced by source paper "reject illegal inputs with generative classifier derived from any discriminative classifier"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "conditional generative models are not robust"
    - "adversarial examples are a natural consequence of test error in noise"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "fixing the train test resolution discrepancy fixefficientnet"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "exploring the limits of weakly supervised pretraining"
    - "imagenet large scale visual recognition challenge"

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)