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: "targeted nonlinear adversarial perturbations in images and videos"
  Abstract: We introduce a method for learning adversarial perturbations targeted to individual images or videos. The learned perturbations are found to be sparse while at the same time containing a high level of feature detail. Thus, the extracted perturbations allow a form of object or action recognition and provide insights into what features the studied deep neural network models consider important when reaching their classification decisions. From an adversarial point of view, the sparse perturbations successfully confused the models into misclassifying, although the perturbed samples still belonged to the same original class by visual examination. This is discussed in terms of a prospective data augmentation scheme. The sparse yet high-quality perturbations may also be leveraged for image or video compression.

Connected through hyperedge HE-1 (induced by source paper "patternless adversarial attacks on video recognition networks"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "sparse adversarial perturbations for videos"
    - "slowfast networks for video recognition"
    - "adversarial perturbations against real time video classification systems"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "a survey on deep learning in medical image analysis"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-3 (induced by source paper "black box adversarial attacks on video recognition models"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "sparse adversarial perturbations for videos"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"

Connected through hyperedge HE-4 (induced by source paper "adversarial framing for image and video classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "adversarial patch"
    - "sparse adversarial perturbations for videos"
    - "lavan localized and visible adversarial noise"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-5 (induced by source paper "regularized adversarial examples for model interpretability"):
  with members:
    - "sparsefool a few pixels make a big difference"
    - "detecting adversarial perturbations with saliency"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "adversarial examples in the physical world"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "adadelta an adaptive learning rate method"

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)