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: "transferable adversarial attacks for image and video object detection"
  Abstract: Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection. However, the existing attacking methods for object detection have two limitations: poor transferability, which denotes that the generated adversarial examples have low success rate to attack other kinds of detection methods, and high computation cost, which means that they need more time to generate an adversarial image, and therefore are difficult to deal with the video data. To address these issues, we utilize a generative mechanism to obtain the adversarial image and video. In this way, the processing time is reduced. To enhance the transferability, we destroy the feature maps extracted from the feature network, which usually constitutes the basis of object detectors. The proposed method is based on the Generative Adversarial Network (GAN) framework, where we combine the high-level class loss and low-level feature loss to jointly train the adversarial example generator. A series of experiments conducted on PASCAL VOC and ImageNet VID datasets show that our method can efficiently generate image and video adversarial examples, and more importantly, these adversarial examples have better transferability, and thus, are able to simultaneously attack two kinds of representative object detection models: proposal based models like Faster-RCNN, and regression based models like SSD.

Connected through hyperedge HE-1 (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-2 (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"
    - "on physical adversarial patches for object detection"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spark spatial aware online incremental attack against visual tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "a twofold siamese network for real time object tracking"

Connected through hyperedge HE-4 (induced by source paper "membership inference attacks against object detection models"):
  with members:
    - "differentially private model publishing for deep learning"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "logan membership inference attacks against generative models"
    - "xgboost a scalable tree boosting system"
    - "ssd single shot multibox detector"
    - "membership inference attacks against machine learning models"

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 "spatial aware online adversarial perturbations against visual object tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "robust adversarial perturbation on deep proposal based models"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"

Connected through hyperedge HE-7 (induced by source paper "making an invisibility cloak real world adversarial attacks on object detectors"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "fcos fully convolutional one stage object detection"
    - "advhat real world adversarial attack on arcface face id system"
    - "adversarial patch"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "focal loss for dense object detection"
    - "on the robustness of semantic segmentation models to adversarial attacks"

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)