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: "robust physical world attacks on machine learning models"
  Abstract: Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world--they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm--Robust Physical Perturbations (RP2)-- that generates perturbations by taking images under different conditions into account. Our algorithm can create spatially-constrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100% of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100% of the testing conditions.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "anomaly detection using one class neural networks"
    - "review of deep learning"

Connected through hyperedge HE-3 (induced by source paper "towards sharper first order adversary with quantized gradients"):
  with members:
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "mixtrain scalable training of formally robust neural networks"
    - "distributionally adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "mitigating adversarial effects through randomization"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on adversarial robustness from optimal transport"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "analyzing federated learning through an adversarial lens"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-5 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-6 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "theoretically principled trade off between robustness and accuracy"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "ember an open dataset for training static pe malware machine learning models"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "daedalus breaking non maximum suppression in object detection via adversarial examples"):
  with members:
    - "semantic adversarial examples"
    - "model reuse attacks on deep learning systems"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "deep learning for generic object detection a survey"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "yolov3 an incremental improvement"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-8 (induced by source paper "a general framework for adversarial examples with objectives"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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.CR(Cryptography and Security)