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 camera stickers a physical camera based attack on deep learning systems"
  Abstract: Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on an object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Our demo video can be viewed at: this https URL

Connected through hyperedge HE-1 (induced by source paper "adversarial data encryption"):
  with members:
    - "convolutional networks with dense connectivity"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "adversarial music real world audio adversary against wake word detection system"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems"):
  with members:
    - "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "autoaugment learning augmentation policies from data"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-3 (induced by source paper "safety concerns and mitigation approaches regarding the use of deep learning in safety critical perception tasks"):
  with members:
    - "hardening of artificial neural networks for use in safety critical applications a mapping study"
    - "on physical adversarial patches for object detection"
    - "scaling provable adversarial defenses"
    - "adversarial patch"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-5 (induced by source paper "scratch that an evolution based adversarial attack against neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "they might not be giants crafting black box adversarial examples with fewer queries using particle swarm optimization"
    - "adversarial patch"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "genattack practical black box attacks with gradient free optimization"
    - "lavan localized and visible adversarial noise"

Connected through hyperedge HE-6 (induced by source paper "adversarial t shirt evading person detectors in a physical world"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "adversarial objects against lidar based autonomous driving systems"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "structured adversarial attack towards general implementation and better interpretability"

Connected through hyperedge HE-7 (induced by source paper "adversarial music real world audio adversary against wake word detection system"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "audio adversarial examples targeted attacks on speech to text"
    - "stress test evaluation for natural language inference"
    - "dolphinattack inaudible voice commands"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "hotflip white box adversarial examples for text classification"

Connected through hyperedge HE-8 (induced by source paper "defending against universal perturbations with shared adversarial training"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "playing the game of universal adversarial perturbations"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "with friends like these who needs adversaries"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "nag network for adversary generation"

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)