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: "shapeshifter robust physical adversarial attack on faster r cnn object detector"
  Abstract: Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we propose ShapeShifter, an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. ShapeShifter can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems. Code related to this paper is available at: https://github.com/shangtse/robust-physical-attack.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"

Connected through hyperedge HE-2 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"

Connected through hyperedge HE-3 (induced by source paper "a little fog for a large turn"):
  with members:
    - "natural adversarial examples"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"
    - "adversarial patch"
    - "exploring the limits of weakly supervised pretraining"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "audio adversarial examples targeted attacks on speech to text"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-4 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "data augmentation with symbolic to real image translation gans for traffic sign recognition"
    - "spatially transformed adversarial examples"
    - "real to virtual domain unification for end to end autonomous driving"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "unmask adversarial detection and defense through robust feature alignment"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "black box adversarial attacks with limited queries and information"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "adversary detection in neural networks via persistent homology"
    - "practical black box attacks against machine learning"
    - "adversarial examples detection in deep networks with convolutional filter statistics"

Connected through hyperedge HE-6 (induced by source paper "massif interactive interpretation of adversarial attacks on deep learning"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "adversarial examples in the physical world"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (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-8 (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"

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)