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 view priors for single view 3d reconstruction"
  Abstract: There is some ambiguity in the 3D shape of an object when the number of observed views is small. Because of this ambiguity, although a 3D object reconstructor can be trained using a single view or a few views per object, reconstructed shapes only fit the observed views and appear incorrect from the unobserved viewpoints. To reconstruct shapes that look reasonable from any viewpoint, we propose to train a discriminator that learns prior knowledge regarding possible views. The discriminator is trained to distinguish the reconstructed views of the observed viewpoints from those of the unobserved viewpoints. The reconstructor is trained to correct unobserved views by fooling the discriminator. Our method outperforms current state-of-the-art methods on both synthetic and natural image datasets; this validates the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency"):
  with members:
    - "photo geometric autoencoding to learn 3d objects from unlabelled images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "scops self supervised co part segmentation"
    - "learning to infer implicit surfaces without 3d supervision"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-2 (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"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "unsupervised 3d pose estimation with geometric self supervision"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning of 3d objects from natural images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning to infer implicit surfaces without 3d supervision"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning category specific mesh reconstruction from image collections"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-4 (induced by source paper "photo geometric autoencoding to learn 3d objects from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "visual object networks image generation with disentangled 3d representation"

Connected through hyperedge HE-5 (induced by source paper "rgbd gan unsupervised 3d representation learning from natural image datasets via rgbd image synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "spectral normalization for generative adversarial networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "few shot generalization for single image 3d reconstruction via priors"):
  with members:
    - "what do single view 3d reconstruction networks learn"
    - "3dn 3d deformation network"
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "low shot learning from imaginary data"
    - "learning to reconstruct shapes from unseen classes"
    - "learning shape priors for single view 3d completion and reconstruction"

Connected through hyperedge HE-7 (induced by source paper "unsupervised generative 3d shape learning from natural images"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "3d guided fine grained face manipulation"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised training for 3d morphable model regression"
    - "a style based generator architecture for generative adversarial networks"
    - "escaping plato s cave using adversarial training 3d shape from unstructured 2d image collections"

Connected through hyperedge HE-8 (induced by source paper "deep meta functionals for shape representation"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "inferring point clouds from single monocular images by depth intermediation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

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)