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: "universal perturbation attack against image retrieval"
  Abstract: Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.

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 "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "open set adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-3 (induced by source paper "walking on the edge fast low distortion adversarial examples"):
  with members:
    - "targeted mismatch adversarial attack query with a flower to retrieve the tower"
    - "adversarial attacks and defences competition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "audio adversarial examples targeted attacks on speech to text"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "adversarial examples for semantic segmentation and object detection"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-4 (induced by source paper "kornia an open source differentiable computer vision library for pytorch"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-5 (induced by source paper "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"):
  with members:
    - "fine tuning cnn image retrieval with no human annotation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"

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)