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: "c3dpo canonical 3d pose networks for non rigid structure from motion"
  Abstract: We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images. We do so by learning a deep network that reconstructs a 3D object from a single view at a time, accounting for partial occlusions, and explicitly factoring the effects of viewpoint changes and object deformations. In order to achieve this factorization, we introduce a novel regularization technique. We first show that the factorization is successful if, and only if, there exists a certain canonicalization function of the reconstructed shapes. Then, we learn the canonicalization function together with the reconstruction one, which constrains the result to be consistent. We demonstrate state-of-the-art reconstruction results for methods that do not use ground-truth 3D supervision for a number of benchmarks, including Up3D and PASCAL3D+. Source code has been made available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "learning the depths of moving people by watching frozen people"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "exploiting temporal context for 3d human pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "how much position information do convolutional neural networks encode"
    - "disentangling monocular 3d object detection"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"
    - "canonical surface mapping via geometric cycle consistency"

Connected through hyperedge HE-3 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "cross domain adaptation for animal pose estimation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "deep non rigid structure from motion"
    - "pytorch an imperative style high performance deep learning library"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "deep nrsfm towards 3d reconstruction in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "deep high resolution representation learning for human pose estimation"
    - "deep non rigid structure from motion"
    - "learning category specific mesh reconstruction from image collections"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "unsupervised generative 3d shape learning from natural images"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"

Connected through hyperedge HE-7 (induced by source paper "animals in virtual environments"):
  with members:
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "3d menagerie modeling the 3d shape and pose of animals"
    - "learning 3d deformation of animals from 2d images"

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)