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: "global and local consistent age generative adversarial networks"
  Abstract: Age progression/regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "stylegan2 distillation for feed forward image manipulation"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "analyzing and improving the image quality of stylegan"
    - "learning continuous face age progression a pyramid of gans"
    - "face to parameter translation for game character auto creation"
    - "meta sim learning to generate synthetic datasets"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "classification accuracy score for conditional generative models"

Connected through hyperedge HE-2 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "a3gan an attribute aware attentive generative adversarial network for face aging"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "attribute aware face aging with wavelet based generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "personalized age progression with bi level aging dictionary learning"
    - "reverse attention for salient object detection"
    - "cbam convolutional block attention module"
    - "attention is all you need"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"

Connected through hyperedge HE-4 (induced by source paper "how old are you face age translation with identity preservation using gans"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "cross view image synthesis using conditional gans"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "uva a universal variational framework for continuous age analysis"):
  with members:
    - "global and local consistent wavelet domain age synthesis"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "disentangled variational representation for heterogeneous face recognition"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "quantifying facial age by posterior of age comparisons"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning face age progression a pyramid architecture of gans"

Connected through hyperedge HE-6 (induced by source paper "controllable face aging"):
  with members:
    - "few shot unsupervised image to image translation"
    - "attribute aware face aging with wavelet based generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning face age progression a pyramid architecture of gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "attribute guided face generation using conditional cyclegan"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "age progression and regression with spatial attention modules"):
  with members:
    - "attribute aware face aging with wavelet based generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-8 (induced by source paper "global and local consistent wavelet domain age synthesis"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "face aging with contextual generative adversarial nets"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "personalized and occupational aware age progression by generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"

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)