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: "fast geometrically perturbed adversarial faces"
  Abstract: The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model. In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces. We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models. To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%. Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%. Code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "spatially transformed adversarial examples"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of face recognition algorithms against attacks and bias"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "adversarial examples are a natural consequence of test error in noise"
    - "deep learning for face recognition pride or prejudiced"
    - "on detecting gans and retouching based synthetic alterations"
    - "on evaluating adversarial robustness"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "recognizing disguised faces in the wild"

Connected through hyperedge HE-3 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry"):
  with members:
    - "robust facial landmark detection via aggregation on geometrically manipulated faces"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"

Connected through hyperedge HE-4 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "targeted nonlinear adversarial perturbations in images and videos"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-5 (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"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-6 (induced by source paper "smoothfool an efficient framework for computing smooth adversarial perturbations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "divide denoise and defend against adversarial attacks"
    - "are adversarial examples inevitable"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-7 (induced by source paper "defending against adversarial iris examples using wavelet decomposition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples to fool iris recognition systems"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial diversity and hard positive generation"
    - "stealing machine learning models via prediction apis"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

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.LG(Machine Learning)