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: "towards automatic image editing learning to see another you"
  Abstract: Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved. Nevertheless, some promising results have been reported in the literature recently,building on deep network architectures. In this work, we zoom in on a specific type of image generation: given an image and knowing the category of objects it belongs to (e.g. faces), our goal is to generate a similar and plausible image, but with some altered attributes. This is particularly challenging, as the model needs to learn to disentangle the effect of each attribute and to apply a desired attribute change to a given input image, while keeping the other attributes and overall object appearance intact. To this end, we learn a convolutional network, where the desired attribute information is encoded then merged with the encoded image at feature map level. We show promising results, both qualitatively as well as quantitatively, in the context of a retrieval experiment, on two face datasets (MultiPie and CAS-PEAL-R1).

Connected through hyperedge HE-1 (induced by source paper "can adversarial networks hallucinate occluded people with a plausible aspect"):
  with members:
    - "occluded person re identification"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "densepose dense human pose estimation in the wild"
    - "perceptual adversarial networks for image to image transformation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "a generative model of people in clothing"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "multi view image generation from a single view"

Connected through hyperedge HE-2 (induced by source paper "boostgan for occlusive profile face frontalization and recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "identity preserving face completion for large ocular region occlusion"
    - "load balanced gans for multi view face image synthesis"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-3 (induced by source paper "deep neural network augmentation generating faces for affect analysis"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "headon real time reenactment of human portrait videos"
    - "geometry contrastive gan for facial expression transfer"
    - "unsupervised training for 3d morphable model regression"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-4 (induced by source paper "photorealistic facial synthesis in the dimensional affect space"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "3d face morphable models in the wild"
    - "age progression regression by conditional adversarial autoencoder"
    - "semantic facial expression editing using autoencoded flow"

Connected through hyperedge HE-5 (induced by source paper "deep learning architectures for face recognition in video surveillance"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an analysis of deep neural network models for practical applications"
    - "towards the design of an end to end automated system for image and video based recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "recover canonical view faces in the wild with deep neural networks"
    - "deep learning face representation by joint identification verification"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-6 (induced by source paper "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"):
  with members:
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep joint face hallucination and recognition"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning residual images for face attribute manipulation"

Connected through hyperedge HE-7 (induced by source paper "photorealistic facial expression synthesis by the conditional difference adversarial autoencoder"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "age progression regression by conditional adversarial autoencoder"
    - "semantic facial expression editing using autoencoded flow"
    - "theano a python framework for fast computation of mathematical expressions"
    - "attribute2image conditional image generation from visual attributes"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-8 (induced by source paper "high resolution image inpainting using multi scale neural patch synthesis"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "generating images with recurrent adversarial networks"
    - "context encoders feature learning by inpainting"
    - "pixel recurrent neural networks"

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)