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: "face deidentification with generative deep neural networks"
  Abstract: Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.

Connected through hyperedge HE-1 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "live face de identification in video"):
  with members:
    - "feature denoising for improving adversarial robustness"
    - "privacy protective gan for face de identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "a hybrid model for identity obfuscation by face replacement"
    - "drit diverse image to image translation via disentangled representations"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "evaluating the effectiveness of automated identity masking aim methods with human perception and a deep convolutional neural network cnn"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "the do s and don ts for cnn based face verification"
    - "triplet probabilistic embedding for face verification and clustering"
    - "deep residual learning for image recognition"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-4 (induced by source paper "de identification without losing faces"):
  with members:
    - "natural and effective obfuscation by head inpainting"
    - "unsupervised image to image translation networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep residual learning for image recognition"
    - "beyond frontal faces improving person recognition using multiple cues"

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)