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 aging effect simulation using hidden factor analysis joint sparse representation"
  Abstract: Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning continuous face age progression a pyramid of gans"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised cross domain image generation"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "age progression and regression with spatial attention modules"):
  with members:
    - "attribute aware face aging with wavelet based generative adversarial networks"
    - "global and local consistent age generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (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"
    - "global and local consistent age generative adversarial networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "attribute aware face aging with wavelet based generative adversarial networks"):
  with members:
    - "age progression and regression with spatial attention modules"
    - "global and local consistent age generative adversarial networks"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "face aging with conditional generative adversarial 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"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "modelling of facial aging and kinship a survey"):
  with members:
    - "hierarchical representation learning for kinship verification"
    - "face aging with conditional generative adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "energy based generative adversarial network"
    - "coupled generative adversarial networks"
    - "family in the wild fiw a large scale kinship recognition database"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "personalized age progression with aging dictionary"

Connected through hyperedge HE-8 (induced by source paper "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"):
  with members:
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "facial aging and rejuvenation by conditional multi adversarial autoencoder with ordinal regression"
    - "multicolumn networks for face recognition"
    - "integrated face analytics networks through cross dataset hybrid training"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "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)