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 attacks and defences competition"
  Abstract: To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

Connected through hyperedge HE-1 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "adversarially robust generalization just requires more unlabeled data"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "robust local features for improving the generalization of adversarial training"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "improving the generalization of adversarial training with domain adaptation"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-2 (induced by source paper "abba saliency regularized motion based adversarial blur attack"):
  with members:
    - "effects of blur and deblurring to visual object tracking"
    - "brain inspired reverse adversarial examples"
    - "spatial aware online adversarial perturbations against visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "amora black box adversarial morphing attack"
    - "learning to synthesize motion blur"
    - "adef an iterative algorithm to construct adversarial deformations"

Connected through hyperedge HE-3 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack"):
  with members:
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-4 (induced by source paper "enhanced adversarial strategically timed attacks against deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "on the use of deep autoencoders for efficient embedded reinforcement learning"
    - "adversarial objects against lidar based autonomous driving systems"
    - "when causal intervention meets adversarial examples and image masking for deep neural networks"
    - "dopamine a research framework for deep reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "baidu apollo em motion planner"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-5 (induced by source paper "image super resolution as a defense against adversarial attacks"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "video salient object detection via fully convolutional networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "improving transferability of adversarial examples with input diversity"

Connected through hyperedge HE-6 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "rethinking softmax cross entropy loss for adversarial robustness"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-8 (induced by source paper "adversarial explanations for understanding image classification decisions and improved neural network robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "on the geometry of adversarial examples"
    - "bag of tricks for image classification with convolutional neural networks"
    - "disentangling adversarial robustness and generalization"
    - "gan dissection visualizing and understanding generative adversarial 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)