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: "a generative model of people in clothing"
  Abstract: We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "textured neural avatars"
    - "neural rendering and reenactment of human actor videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "dense intrinsic appearance flow for human pose transfer"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-3 (induced by source paper "learning to transfer texture from clothing images to 3d humans"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "tex2shape detailed full human body geometry from a single image"
    - "unsupervised person image generation with semantic parsing transformation"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-4 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "unsupervised learning of object structure and dynamics from videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-5 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "generative face completion"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-6 (induced by source paper "garmentgan photo realistic adversarial fashion transfer"):
  with members:
    - "unpaired pose guided human image generation"
    - "compatible and diverse fashion image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "unsupervised person image synthesis in arbitrary poses"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "spectral normalization for generative adversarial networks"
    - "viton an image based virtual try on network"

Connected through hyperedge HE-7 (induced by source paper "swaptext image based texts transfer in scenes"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "ga dan geometry aware domain adaptation network for scene text detection and recognition"
    - "symmetry constrained rectification network for scene text recognition"
    - "arbitrary style transfer with style attentional networks"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "spatial fusion gan for image synthesis"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "tailornet predicting clothing in 3d as a function of human pose shape and garment style"):
  with members:
    - "learning to transfer texture from clothing images to 3d humans"
    - "tex2shape detailed full human body geometry from a single image"
    - "amass archive of motion capture as surface shapes"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "multi garment net learning to dress 3d people from images"
    - "fashion minimal edits for outfit improvement"
    - "convolutional mesh regression for single image human shape reconstruction"

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)