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: "high quality facial surface and texture synthesis via generative adversarial networks"
  Abstract: In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures. Perhaps the best known of its kind is the linear 3D morphable model (3DMM) for faces. Such models can be found at the core of many computer vision applications such as face reconstruction, recognition and authentication to name just a few. #R##N#Generative adversarial networks (GANs) have shown great promise in imitating high dimensional data distributions. State of the art GANs are capable of performing tasks such as image to image translation as well as auditory and image signal synthesis, producing novel plausible samples from the data distribution at hand. #R##N#Geometric data is generally more difficult to process due to the inherent lack of an intrinsic parametrization. By bringing geometric data into an aligned space, we are able to map the data onto a 2D plane using a universal parametrization. This alignment process allows for efficient processing of digitally scanned geometric data via image processing tools. #R##N#Using this methodology, we propose a novel face synthesis model for generation of realistic facial textures together with their corresponding geometry. A GAN is employed in order to imitate the space of parametrized human textures, while corresponding facial geometries are generated by learning the best 3DMM coefficients for each texture. The generated textures are mapped back onto the corresponding geometries to obtain new generated high resolution 3D faces.

Connected through hyperedge HE-1 (induced by source paper "sean image synthesis with semantic region adaptive normalization"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "controllable artistic text style transfer via shape matching gan"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mask guided portrait editing with conditional gans"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "non stationary texture synthesis by adversarial expansion"

Connected through hyperedge HE-2 (induced by source paper "synthesizing coupled 3d face modalities by trunk branch generative adversarial networks"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "representation learning by rotating your faces"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting overfitting of deep generative networks via latent recovery"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot video to video synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"):
  with members:
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "deep appearance models for face rendering"
    - "nonlinear 3d face morphable model"
    - "progressive growing of gans for improved quality stability and variation"
    - "unrestricted facial geometry reconstruction using image to image translation"

Connected through hyperedge HE-5 (induced by source paper "image2stylegan how to embed images into the stylegan latent space"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "latent filter scaling for multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-6 (induced by source paper "synthesizing facial photometries and corresponding geometries using generative adversarial networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "densereg fully convolutional dense shape regression in the wild"
    - "efficient inter geodesic distance computation and fast classical scaling"
    - "nonlinear 3d face morphable model"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-7 (induced by source paper "a decoupled 3d facial shape model by adversarial training"):
  with members:
    - "synthesizing facial photometries and corresponding geometries using generative adversarial networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "ganimation anatomically aware facial animation from a single image"
    - "nonlinear 3d face morphable model"
    - "improved training of wasserstein gans"
    - "uv gan adversarial facial uv map completion for pose invariant face recognition"
    - "semantically decomposing the latent spaces of generative adversarial 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.CG(Computational Geometry)