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: "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
  Abstract: We propose DoubleFusion, a new real-time system that combines volumetric dynamic reconstruction with data-driven template fitting to simultaneously reconstruct detailed geometry, non-rigid motion and the inner human body shape from a single depth camera. One of the key contributions of this method is a double layer representation consisting of a complete parametric body shape inside, and a gradually fused outer surface layer. A pre-defined node graph on the body surface parameterizes the non-rigid deformations near the body, and a free-form dynamically changing graph parameterizes the outer surface layer far from the body, which allows more general reconstruction. We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance. Moreover, the inner body shape is optimized online and forced to fit inside the outer surface layer. Overall, our method enables increasingly denoised, detailed and complete surface reconstructions, fast motion tracking performance and plausible inner body shape reconstruction in real-time. In particular, experiments show improved fast motion tracking and loop closure performance on more challenging scenarios.

Connected through hyperedge HE-1 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "tex2shape detailed full human body geometry from a single image"
    - "geometric disentanglement for generative latent shape models"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"

Connected through hyperedge HE-2 (induced by source paper "deepcap monocular human performance capture using weak supervision"):
  with members:
    - "tailornet 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"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "textured neural avatars"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "deep video portraits"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "densereg fully convolutional dense shape regression in the wild"
    - "deep video based performance cloning"
    - "lookingood enhancing performance capture with real time neural re rendering"

Connected through hyperedge HE-4 (induced by source paper "deephuman 3d human reconstruction from a single image"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "dense 3d object reconstruction from a single depth view"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "video based reconstruction of 3d people models"
    - "3d human body reconstruction from a single image via volumetric regression"

Connected through hyperedge HE-5 (induced by source paper "eventcap monocular 3d capture of high speed human motions using an event camera"):
  with members:
    - "event based vision a survey"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "temporal upsampling of depth maps using a hybrid camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "asynchronous photometric feature tracking using events and frames"
    - "bringing a blurry frame alive at high frame rate with an event camera"

Connected through hyperedge HE-6 (induced by source paper "temporally coherent full 3d mesh human pose recovery from monocular video"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "video based reconstruction of 3d people models"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "detailed human avatars from monocular video"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "towards 3d human shape recovery under clothing"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "monocular total capture posing face body and hands in the wild"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "a generative model of people in clothing"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "hand keypoint detection in single images using multiview bootstrapping"

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)