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 robustness may be at odds with simplicity"
  Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Towards explaining this gap, we highlight the hypothesis that $\textit{robust classification may require more complex classifiers (i.e. more capacity) than standard classification.}$ #R##N#In this note, we show that this hypothesis is indeed possible, by giving several theoretical examples of classification tasks and sets of "simple" classifiers for which: (1) There exists a simple classifier with high standard accuracy, and also high accuracy under random $\ell_\infty$ noise. (2) Any simple classifier is not robust: it must have high adversarial loss with $\ell_\infty$ perturbations. (3) Robust classification is possible, but only with more complex classifiers (exponentially more complex, in some examples). #R##N#Moreover, $\textit{there is a quantitative trade-off between robustness and standard accuracy among simple classifiers.}$ This suggests an alternate explanation of this phenomenon, which appears in practice: the tradeoff may occur not because the classification task inherently requires such a tradeoff (as in [Tsipras-Santurkar-Engstrom-Turner-Madry `18]), but because the structure of our current classifiers imposes such a tradeoff.

Connected through hyperedge HE-1 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "defensive quantization when efficiency meets robustness"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"

Connected through hyperedge HE-2 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models"):
  with members:
    - "adversarially robust learning could leverage computational hardness"
    - "lower bounds for adversarially robust pac learning"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-3 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-4 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "label propagation for deep semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "deep metric transfer for label propagation with limited annotated data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "adversarial dropout for supervised and semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "understanding and mitigating the tradeoff between robustness and accuracy"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "a fourier perspective on model robustness in computer vision"
    - "generating natural language adversarial examples"
    - "adversarially robust generalization requires more data"
    - "exploring the landscape of spatial robustness"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"

Connected through hyperedge HE-6 (induced by source paper "attacks which do not kill training make adversarial learning stronger"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-8 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

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.LG(Machine Learning)