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: "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
  Abstract: The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition. However, recent studies have highlighted the lack of robustness in well-trained deep neural networks to adversarial examples. Visually imperceptible perturbations to natural images can easily be crafted and mislead the image classifiers towards misclassification. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Our extensive experimental results reveal several new insights: (1) linear scaling law - the empirical $\ell_2$ and $\ell_\infty$ distortion metrics scale linearly with the logarithm of classification error; (2) model architecture is a more critical factor to robustness than model size, and the disclosed accuracy-robustness Pareto frontier can be used as an evaluation criterion for ImageNet model designers; (3) for a similar network architecture, increasing network depth slightly improves robustness in $\ell_\infty$ distortion; (4) there exist models (in VGG family) that exhibit high adversarial transferability, while most adversarial examples crafted from one model can only be transferred within the same family. Experiment code is publicly available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-2 (induced by source paper "improving the affordability of robustness training for dnns"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "exploring the hyperparameter landscape of adversarial robustness"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "adversarially robust transfer learning"
    - "scaling provable adversarial defenses"
    - "curriculum adversarial training"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-3 (induced by source paper "advms a multi source multi cost defense against adversarial attacks"):
  with members:
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "adversarial t shirt evading person detectors in a physical world"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "breaking transferability of adversarial samples with randomness"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-5 (induced by source paper "theoretically principled trade off between robustness and accuracy"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "adversarial vision challenge"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-6 (induced by source paper "what do adversarially robust models look at"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling provable adversarial defenses"
    - "a dual approach to scalable verification of deep networks"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"

Connected through hyperedge HE-7 (induced by source paper "enhancing adversarial defense by k winners take all"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "hopskipjumpattack a query efficient decision based attack"
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-8 (induced by source paper "interpreting adversarial examples with attributes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"

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)