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: "style transfer for anime sketches with enhanced residual u net and auxiliary classifier gan"
  Abstract: Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images. However, when it comes to the task of applying a painting's style to an anime sketch, these methods will just randomly colorize sketch lines as outputs and fail in the main task: specific style tranfer. In this paper, we integrated residual U-net to apply the style to the gray-scale sketch with auxiliary classifier generative adversarial network (AC-GAN). The whole process is automatic and fast, and the results are creditable in the quality of art style as well as colorization.

Connected through hyperedge HE-1 (induced by source paper "adversarial colorization of icons based on structure and color conditions"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "conditional image to image translation"
    - "semi supervised fusedgan for conditional image generation"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "user guided deep anime line art colorization with conditional adversarial networks"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comicolorization semi automatic manga colorization"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "dilated residual networks"
    - "real time user guided image colorization with learned deep priors"

Connected through hyperedge HE-3 (induced by source paper "twin gan unpaired cross domain image translation with weight sharing gans"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep painterly harmonization"
    - "on convergence and stability of gans"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "modulating early visual processing by language"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-4 (induced by source paper "editable generative adversarial networks generating and editing faces simultaneously"):
  with members:
    - "high quality bidirectional generative adversarial networks"
    - "mask aware photorealistic face attribute manipulation"
    - "on convergence and stability of gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triple generative adversarial nets"
    - "semi latent gan learning to generate and modify facial images from attributes"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-5 (induced by source paper "a survey of comics research in computer science"):
  with members:
    - "using posters to recommend anime and mangas in a cold start scenario"
    - "comicolorization semi automatic manga colorization"
    - "sketch based manga retrieval using manga109 dataset"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "obamanet photo realistic lip sync from text"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual to sound generating natural sound for videos in the wild"

Connected through hyperedge HE-6 (induced by source paper "photo to caricature translation on faces in the wild"):
  with members:
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "began boundary equilibrium generative adversarial networks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "image de raining using a conditional generative adversarial network"
    - "dualgan unsupervised dual learning for image to image translation"

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)