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: "robust adversarial perturbation on deep proposal based models"
  Abstract: Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms. In this paper, we describe a robust adversarial perturbation (R-AP) method to attack deep proposal-based object detectors and instance segmentation algorithms. Our method focuses on attacking the common component in these algorithms, namely Region Proposal Network (RPN), to universally degrade their performance in a black-box fashion. To do so, we design a loss function that combines a label loss and a novel shape loss, and optimize it with respect to image using a gradient based iterative algorithm. Evaluations are performed on the MS COCO 2014 dataset for the adversarial attacking of 6 state-of-the-art object detectors and 2 instance segmentation algorithms. Experimental results demonstrate the efficacy of the proposed method.

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"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "towards adversarially robust object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "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"
    - "mdssd multi scale deconvolutional single shot detector for small objects"

Connected through hyperedge HE-3 (induced by source paper "upc learning universal physical camouflage attacks on object detectors"):
  with members:
    - "humans can decipher adversarial images"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "textbugger generating adversarial text against real world applications"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "dpatch an adversarial patch attack on object detectors"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "exploring the vulnerability of single shot module in object detectors via imperceptible background patches"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "lavan localized and visible adversarial noise"
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "fssd feature fusion single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "transferable adversarial attacks for image and video object detection"):
  with members:
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "optimizing video object detection via a scale time lattice"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "sparse adversarial perturbations for videos"
    - "generating adversarial examples with adversarial networks"
    - "certified defenses against adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "robust physical world attacks on deep learning models"

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)