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 a shared shape space for multimodal garment design"
  Abstract: Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realistic dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input {\em without} requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics.

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 "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"
    - "learning based animation of clothing for virtual try on"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (induced by source paper "cloth in the wind a case study of physical measurement through simulation"):
  with members:
    - "seeing tree structure from vibration"
    - "in defense of the triplet loss for person re identification"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "conditional similarity networks"
    - "what happens if learning to predict the effect of forces in images"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "deep feature learning with relative distance comparison for person re identification"
    - "deep residual learning for image recognition"

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 "360 degree textures of people in clothing from a single image"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "textured neural avatars"
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-7 (induced by source paper "tex2shape detailed full human body geometry from a single image"):
  with members:
    - "relighting humans occlusion aware inverse rendering for full body human images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "simulcap single view human performance capture with cloth simulation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "fml face model learning from videos"

Connected through hyperedge HE-8 (induced by source paper "3d virtual garment modeling from rgb images"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "pose guided fashion image synthesis using deep generative model"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "video based reconstruction of 3d people models"
    - "siclope silhouette based clothed people"
    - "dense pose transfer"

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.GR(Graphics)