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: "unsupervised creation of parameterized avatars"
  Abstract: We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping's objective is to have the output image as similar as possible to the input image. During training, no supervision is given in the form of matching inputs and outputs. #R##N#This learning problem extends two literature problems: unsupervised domain adaptation and cross domain transfer. We define a generalization bound that is based on discrepancy, and employ a GAN to implement a network solution that corresponds to this bound. Experimentally, our method is shown to solve the problem of automatically creating avatars.

Connected through hyperedge HE-1 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-2 (induced by source paper "attribute driven spontaneous motion in unpaired image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "improving shape deformation in unsupervised image to image translation"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "semi parametric image synthesis"

Connected through hyperedge HE-3 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "face to parameter translation for game character auto creation"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "arbitrary style transfer with deep feature reshuffle"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "end to end 3d face reconstruction with deep neural networks"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "extreme 3d face reconstruction seeing through occlusions"

Connected through hyperedge HE-5 (induced by source paper "landmark assisted cyclegan for cartoon face generation"):
  with members:
    - "cgans with projection discriminator"
    - "multimodal unsupervised image to image translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "the contextual loss for image transformation with non aligned data"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "gansynth adversarial neural audio synthesis"):
  with members:
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "sing symbol to instrument neural generator"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"

Connected through hyperedge HE-7 (induced by source paper "multimodal unsupervised image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a closed form solution to photorealistic image stylization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "multi agent diverse generative adversarial networks"
    - "style transfer from non parallel text by cross alignment"
    - "decomposing motion and content for natural video sequence prediction"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"):
  with members:
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "began boundary equilibrium generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "sphereface deep hypersphere embedding for face recognition"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning deep convolutional embeddings for face representation using joint sample and set based supervision"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"

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)