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: "learning residual images for face attribute manipulation"
  Abstract: Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value. Instead of manipulating the whole image, we propose to learn the corresponding residual image defined as the difference between images before and after the manipulation. In this way, the manipulation can be operated efficiently with modest pixel modification. The framework of our approach is based on the Generative Adversarial Network. It consists of two image transformation networks and a discriminative network. The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images. We also apply dual learning to allow transformation networks to learn from each other. Experiments show that residual images can be effectively learned and used for attribute manipulations. The generated images remain most of the details in attribute-irrelevant areas.

Connected through hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "detecting gan generated fake images using co occurrence matrices"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"

Connected through hyperedge HE-2 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "multi label image recognition with graph convolutional networks"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-3 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses"):
  with members:
    - "suppressing uncertainties for large scale facial expression recognition"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep facial expression recognition a survey"
    - "spectral normalization for generative adversarial networks"
    - "geometry contrastive gan for facial expression transfer"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "large scale gan training for high fidelity natural image synthesis"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-4 (induced by source paper "tailorgan making user defined fashion designs"):
  with members:
    - "lip movements generation at a glance"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "sequential attention gan for interactive image editing via dialogue"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-5 (induced by source paper "dotfan a domain transferred face augmentation network for pose and illumination invariant face recognition"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "driver gaze estimation in the real world overcoming the eyeglass challenge"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "unsupervised eyeglasses removal in the wild"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis"
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "compatible and diverse fashion image inpainting"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "learning type aware embeddings for fashion compatibility"
    - "design design inspiration from generative networks"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "language guided fashion image manipulation with feature wise transformations"

Connected through hyperedge HE-8 (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"
    - "fast geometrically perturbed adversarial faces"
    - "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"

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)