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: "learning based animation of clothing for virtual try on"
  Abstract: This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "towards photo realistic virtual try on by adaptively generating leftrightarrow preserving image content"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "fashion minimal edits for outfit improvement"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "fashion editing with multi scale attention normalization"
    - "pose guided fashion image synthesis using deep generative model"
    - "toward characteristic preserving image based virtual try on network"

Connected through hyperedge HE-3 (induced by source paper "the virtual tailor 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"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-4 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-5 (induced by source paper "learning to dress 3d people in generative clothing"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "learning to train with synthetic humans"

Connected through hyperedge HE-6 (induced by source paper "dressing for diverse body shapes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sizenet weakly supervised learning of visual size and fit in fashion images"
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "3d virtual garment modeling from rgb images"
    - "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"
    - "a deep learning system for predicting size and fit in fashion e commerce"

Connected through hyperedge HE-7 (induced by source paper "multi garment net learning to dress 3d people from images"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "simulcap single view human performance capture with cloth simulation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "instance level human parsing via part grouping network"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-8 (induced by source paper "garnet a two stream network for fast and accurate 3d cloth draping"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "video based reconstruction of 3d people models"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "learning a shared shape space for multimodal garment design"
    - "pu net point cloud upsampling network"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"

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)