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: "data augmentation instead of explicit regularization"
  Abstract: Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as dropout and weight decay, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of results, they seem to waste capacity. In contrast, data augmentation techniques reduce the generalization error by increasing the number of training examples and without reducing the effective capacity. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone---without any other explicit regularization techniques---can achieve the same performance or higher as regularized models, especially when training with fewer examples.

Connected through hyperedge HE-1 (induced by source paper "data augmentation with mobius transformations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "enabling explainable fusion in deep learning with fuzzy integral neural networks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-2 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "fast autoaugment"):
  with members:
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "regularized evolution for image classifier architecture search"
    - "shakedrop regularization for deep residual learning"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"
    - "large batch training of convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "safe augmentation learning task specific transformations from data"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "adversarial learning of general transformations for data augmentation"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "do deep nets really need weight decay and dropout"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-6 (induced by source paper "automated brain extraction of multisequence mri using artificial neural networks"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "deepneuro an open source deep learning toolbox for neuroimaging"
    - "compnet complementary segmentation network for brain mri extraction"
    - "brain extraction from normal and pathological images a joint pca image reconstruction approach"
    - "imperfect segmentation labels how much do they matter"
    - "cnn based segmentation of medical imaging data"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning robust visual representations using data augmentation invariance"):
  with members:
    - "making convolutional networks shift invariant again"
    - "augment your batch better training with larger batches"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "group equivariant convolutional networks"
    - "towards biologically plausible deep learning"
    - "striving for simplicity the all convolutional net"
    - "intriguing properties of neural networks"

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)