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: "note on attacking object detectors with adversarial stickers"
  Abstract: Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled. This can be problematic when deep learning is used to assist in safety critical decisions. Recent research has shown that classifiers can be attacked by physical adversarial examples under various physical conditions. Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples. In this note, we briefly show both static and dynamic test results. We design an algorithm that produces physical adversarial inputs, which can fool the YOLO object detector and can also attack Faster-RCNN with relatively high success rate based on transferability. Furthermore, our algorithm can compress the size of the adversarial inputs to stickers that, when attached to the targeted object, result in the detector either mislabeling or not detecting the object a high percentage of the time. This note provides a small set of results. Our upcoming paper will contain a thorough evaluation on other object detectors, and will present the algorithm.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "design and interpretation of universal adversarial patches in face detection"):
  with members:
    - "towards adversarially robust object detection"
    - "theoretically principled trade off between robustness and accuracy"
    - "a general framework for adversarial examples with objectives"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "on physical adversarial patches for object detection"
    - "advhat real world adversarial attack on arcface face id system"
    - "dsfd dual shot face detector"

Connected through hyperedge HE-4 (induced by source paper "robust synthesis of adversarial visual examples using a deep image prior"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deep image prior"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-5 (induced by source paper "shield fast practical defense and vaccination for deep learning using jpeg compression"):
  with members:
    - "provable limitations of deep learning"
    - "dense associative memory is robust to adversarial inputs"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-6 (induced by source paper "shapeshifter robust physical adversarial attack on faster r cnn object detector"):
  with members:
    - "adversarial patch"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "robust physical world attacks on machine learning models"

Connected through hyperedge HE-7 (induced by source paper "deepbillboard systematic physical world testing of autonomous driving systems"):
  with members:
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "synthesizing robust adversarial examples"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "adversarial examples for semantic segmentation and object detection"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-8 (induced by source paper "verisimilar percept sequences tests for autonomous driving intelligent agent assessment"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "blaming humans in autonomous vehicle accidents shared responsibility across levels of automation"
    - "regularized evolution for image classifier architecture search"
    - "driving simulator platform for development and evaluation of safety and emergency systems"
    - "adversarial logit pairing"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image recognition"

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)