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: "volumetric performance capture from minimal camera viewpoints"
  Abstract: We present a convolutional autoencoder that enables high fidelity volumetric reconstructions of human performance to be captured from multi-view video comprising only a small set of camera views. Our method yields similar end-to-end reconstruction error to that of a probabilistic visual hull computed using significantly more (double or more) viewpoints. We use a deep prior implicitly learned by the autoencoder trained over a dataset of view-ablated multi-view video footage of a wide range of subjects and actions. This opens up the possibility of high-end volumetric performance capture in on-set and prosumer scenarios where time or cost prohibit a high witness camera count.

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 "pifu pixel aligned implicit function for high resolution clothed human digitization"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning category specific mesh reconstruction from image collections"
    - "group normalization"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-3 (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-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 "moulding humans non parametric 3d human shape estimation from single images"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "group normalization"
    - "image based synthesis for deep 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "densepose dense human pose estimation in the wild"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-6 (induced by source paper "semantic estimation of 3d body shape and pose using minimal cameras"):
  with members:
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "exploiting temporal information for 3d human pose estimation"
    - "volumetric super resolution of multispectral data"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"

Connected through hyperedge HE-7 (induced by source paper "siclope silhouette based clothed people"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "synthesizing images of humans in unseen poses"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "photo wake up 3d character animation from a single photo"
    - "view extrapolation of human body from a single image"
    - "3d human pose estimation in the wild by adversarial learning"

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)