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: "towards the automatic anime characters creation with generative adversarial networks"
  Abstract: Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN. With quantitative analysis and case studies we demonstrate that our efforts lead to a stable and high-quality model. Moreover, to assist people with anime character design, we build a website (this http URL) with our pre-trained model available online, which makes the model easily accessible to general public.

Connected through hyperedge HE-1 (induced by source paper "learning diverse stochastic human action generators by learning smooth latent transitions"):
  with members:
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deep video generation prediction and completion of human action sequences"
    - "human action generation with generative adversarial networks"
    - "stochastic adversarial video prediction"
    - "recurrent transition networks for character locomotion"
    - "combining recurrent neural networks and adversarial training for human motion synthesis and control"
    - "dual motion gan for future flow embedded video prediction"

Connected through hyperedge HE-2 (induced by source paper "pi rec progressive image reconstruction network with edge and color domain"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "smit stochastic multi label image to image translation"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "recycle gan unsupervised video retargeting"
    - "deep contextualized word representations"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "everybody dance now"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "learning gans and ensembles using discrepancy"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "efficient gan based anomaly detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

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 "encoding invariances in deep generative models"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "spatially constrained generative adversarial networks for conditional image generation"
    - "constrained image generation using binarized neural networks with decision procedures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "do gan loss functions really matter"
    - "on convergence and stability of gans"
    - "bias and generalization in deep generative models an empirical study"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-8 (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"
    - "global and local consistent age generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

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)