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: "unravelling robustness of deep learning based face recognition against adversarial attacks"
  Abstract: Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, including OpenFace and VGG-Face, and two publicly available databases (MEDS and PaSC) demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. The proposed method is also compared with existing detection algorithms and the results show that it is able to detect the attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present several effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.

Connected through hyperedge HE-1 (induced by source paper "detecting face2face facial reenactment in videos"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "headon real time reenactment of human portrait videos"
    - "mesonet a compact facial video forgery detection network"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "two stream neural networks for tampered face detection"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive overview of biometric fusion"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"

Connected through hyperedge HE-3 (induced by source paper "minimum uncertainty based detection of adversaries in deep neural networks"):
  with members:
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "dolphinattack inaudible voice commands"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "yolo9000 better faster stronger"
    - "universal adversarial perturbations"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "attacks on state of the art face recognition using attentional adversarial attack generative network"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "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"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "additive margin softmax for face verification"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-5 (induced by source paper "simultaneous adversarial training learn from others mistakes"):
  with members:
    - "fast geometrically perturbed adversarial faces"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "local deep neural networks for age and gender classification"
    - "adversarial and clean data are not twins"
    - "magnet a two pronged defense against adversarial examples"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples in deep learning characterization and divergence"):
  with members:
    - "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "blind pre processing a robust defense method against adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "robust pre processing a robust defense method against adversary attack"
    - "sok security and privacy in machine learning"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-7 (induced by source paper "fast geometrically perturbed adversarial faces"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "spatially transformed adversarial examples"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "safer classification by synthesis"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning adversary resistant deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "recognizing disguised faces in the wild"):
  with members:
    - "representation learning by rotating your faces"
    - "disguisenet a contrastive approach for disguised face verification in the wild"
    - "on matching faces with alterations due to plastic surgery and disguise"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "recurrent scale approximation for object detection in cnn"
    - "level playing field for million scale face recognition"

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)