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: "attacks on state of the art face recognition using attentional adversarial attack generative network"
  Abstract: With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. So, it is important to study how face recognition networks are subject to attacks. In this paper, we focus on a novel way to do attacks against face recognition network that misleads the network to identify someone as the target person not misclassify inconspicuously. Simultaneously, for this purpose, we introduce a specific attentional adversarial attack generative network to generate fake face images. For capturing the semantic information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces face recognition networks as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning with margin based triplet embedding regularization"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "deep facial expression recognition a survey"
    - "deep defense training dnns with improved adversarial robustness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "advfaces adversarial face synthesis"):
  with members:
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "a general framework for adversarial examples with objectives"
    - "spatially transformed adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-3 (induced by source paper "amora black box adversarial morphing attack"):
  with members:
    - "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"
    - "advfaces adversarial face synthesis"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "attacking optical flow"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "machine learning testing survey landscapes and horizons"

Connected through hyperedge HE-4 (induced by source paper "detection of face recognition adversarial attacks"):
  with members:
    - "adversarial image translation unrestricted adversarial examples in face recognition systems"
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "on the robustness of deep k nearest neighbors"
    - "on adversarial patches real world attack on arcface 100 face recognition system"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep face recognition a survey"
    - "picture it in your mind generating high level visual representations from textual descriptions"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-5 (induced by source paper "adaptive generation of unrestricted adversarial inputs"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "a general framework for adversarial examples with objectives"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "global adversarial attacks for assessing deep learning robustness"
    - "how good is my gan"
    - "rmdl random multimodel deep learning for classification"
    - "towards the first adversarially robust neural network model on mnist"

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)