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 examples that fool detectors"
  Abstract: An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial.

Connected through hyperedge HE-1 (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-2 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-3 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-4 (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-5 (induced by source paper "towards adversarially robust object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "robust adversarial perturbation on deep proposal based models"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "feature denoising for improving adversarial robustness"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "yolov3 an incremental improvement"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-6 (induced by source paper "advhat real world adversarial attack on arcface face id system"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-7 (induced by source paper "adversarial examples versus cloud based detectors a black box empirical study"):
  with members:
    - "exploring the space of black box attacks on deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"
    - "google s cloud vision api is not robust to noise"
    - "query efficient black box adversarial examples superceded"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-8 (induced by source paper "fooling detection alone is not enough first adversarial attack against multiple object tracking"):
  with members:
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "practical adversarial attack against object detector"

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)