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: "volumedeform real time volumetric non rigid reconstruction"
  Abstract: We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method does not require a pre-defined shape template to start with and builds up the scene model from scratch during the scanning process. Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation. Motion tracking is based on a set of extracted sparse color features in combination with a dense depth-based constraint formulation. This enables accurate tracking and drastically reduces drift inherent to standard model-to-depth alignment. We cast finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints. The problem is tackled in real-time at the camera's capture rate using a data-parallel flip-flop optimization strategy. Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "deferred neural rendering image synthesis using neural textures"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "layer structured 3d scene inference via view synthesis"
    - "ignor image guided neural object rendering"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-4 (induced by source paper "learning to reconstruct people in clothing from a single rgb camera"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "instance level human parsing via part grouping network"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-5 (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"
    - "learning based animation of clothing for virtual try on"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "flownet3d geometric losses for deep scene flow estimation"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "deep rigid instance scene flow"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "pyramid stereo matching network"

Connected through hyperedge HE-7 (induced by source paper "neural volumes learning dynamic renderable volumes from images"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deep appearance models for face rendering"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-8 (induced by source paper "simulcap single view human performance capture with cloth simulation"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "articulatedfusion real time reconstruction of motion geometry and segmentation using a single depth camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "detailed human avatars from monocular video"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "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)