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: "on physical adversarial patches for object detection"
  Abstract: In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected objects in the image. That is, we can place the patch anywhere in the image, causing all existing objects in the image to be missed entirely by the detector, even those far away from the patch itself. This in turn opens up new lines of physical attacks against object detection systems, which require no modification of the objects in a scene. A demo of the system can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "bag of freebies for training object detection neural networks"
    - "learning to generate synthetic data via compositing"
    - "mixmatch a holistic approach to semi supervised learning"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "regularization for deep learning a taxonomy"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (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"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "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-3 (induced by source paper "grid saliency for context explanations of semantic segmentation"):
  with members:
    - "interpreting adversarial examples with attributes"
    - "fast scnn fast semantic segmentation network"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "context proposals for saliency detection"
    - "guided upsampling network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-4 (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"
    - "advhat real world adversarial attack on arcface face id system"
    - "dsfd dual shot face detector"
    - "adversarial patch"

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)