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: "deep single view 3d object reconstruction with visual hull embedding"
  Abstract: 3D object reconstruction is a fundamental task of many robotics and AI problems. With the aid of deep convolutional neural networks (CNNs), 3D object reconstruction has witnessed a significant progress in recent years. However, possibly due to the prohibitively high dimension of the 3D object space, the results from deep CNNs are often prone to missing some shape details. In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network. Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls. Experiment on both synthetic data and real images show that embedding a single-view visual hull for shape refinement can significantly improve the reconstruction quality by recovering more shapes details and improving shape consistency with the input image.

Connected through hyperedge HE-1 (induced by source paper "escaping plato s cave 3d shape from adversarial rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "escaping plato s cave using adversarial training 3d shape from unstructured 2d image collections"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "3d shape reconstruction from a single 2d image via 2d 3d self consistency"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

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)