human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adversarial ranking attack and defense"):
  Abstract: Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations. Specifically, the expected ranking order is first represented as a set of inequalities, and then a triplet-like objective function is designed to obtain the optimal perturbation. Conversely, a defense method is also proposed to improve the ranking system robustness, which can mitigate all the proposed attacks simultaneously. Our adversarial ranking attacks and defense are evaluated on datasets including MNIST, Fashion-MNIST, and Stanford-Online-Products. Experimental results demonstrate that a typical deep ranking system can be effectively compromised by our attacks. Meanwhile, the system robustness can be moderately improved with our defense. Furthermore, the transferable and universal properties of our adversary illustrate the possibility of realistic black-box attack.

Includes members:
  Member "curls whey boosting black box adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "metric learning with horde high order regularizer for deep embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "sparse and imperceivable adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "square attack a query efficient black box adversarial attack via random search")
  Member "who s afraid of adversarial queries the impact of image modifications on content based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards large yet imperceptible adversarial image perturbations with perceptual color distance")
  Member "hopskipjumpattack a query efficient decision based attack":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "input validation for neural networks via runtime local robustness verification")
  Member "towards adversarially robust object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "defense against adversarial images using web scale nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

Question: Please tell me which class the source paper 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)