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: "improved mixed example data augmentation"
  Abstract: In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving transformations of existing training examples. While these types of transformations make intuitive sense, recent work has demonstrated that even non-label-preserving data augmentation can be surprisingly effective, examining this type of data augmentation through linear combinations of pairs of examples. Despite their effectiveness, little is known about why such methods work. In this work, we aim to explore a new, more generalized form of this type of data augmentation in order to determine whether such linearity is necessary. By considering this broader scope of "mixed-example data augmentation", we find a much larger space of practical augmentation techniques, including methods that improve upon previous state-of-the-art. This generalization has benefits beyond the promise of improved performance, revealing a number of types of mixed-example data augmentation that are radically different from those considered in prior work, which provides evidence that current theories for the effectiveness of such methods are incomplete and suggests that any such theory must explain a much broader phenomenon. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "adversarial data encryption"):
  with members:
    - "convolutional networks with dense connectivity"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial music real world audio adversary against wake word detection system"

Connected through hyperedge HE-2 (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"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cutmix regularization strategy to train strong classifiers with localizable features"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "dropblock a regularization method for convolutional networks"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial complementary learning for weakly supervised object localization"
    - "shakedrop regularization for deep residual learning"
    - "exploring the limits of weakly supervised pretraining"
    - "mixup as locally linear out of manifold regularization"

Connected through hyperedge HE-5 (induced by source paper "improved adversarial robustness via logit regularization methods"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "data augmentation by pairing samples for images classification"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "autoaugment learning augmentation policies from data"
    - "adversarially robust generalization requires more data"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "motivating the rules of the game for adversarial example research"

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)