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: "attribute aware face aging with wavelet based generative adversarial networks"
  Abstract: Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to unnatural changes of facial attributes during the aging process, which could not be solved by only enforcing identity consistency like most existing studies do. In this paper, we propose a attribute-aware face aging model with wavelet-based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model to synthesize visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing datasets.

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 "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 "a3gan an attribute aware attentive generative adversarial network for face aging"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "global and local consistent age 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 "wavelets to the rescue improving sample quality of latent variable deep generative models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "pioneer networks progressively growing generative autoencoder"
    - "isolating sources of disentanglement in variational autoencoders"
    - "generative networks as inverse problems with scattering transforms"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "controllable face aging"):
  with members:
    - "few shot unsupervised image to image translation"
    - "global and local consistent age 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-6 (induced by source paper "age progression and regression with spatial attention modules"):
  with members:
    - "global and local consistent age 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-7 (induced by source paper "look globally age locally face aging with an attention mechanism"):
  with members:
    - "learning face age progression a pyramid architecture of gans"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "joint face detection and alignment using multitask cascaded convolutional 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)