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: "compact model representation for 3d reconstruction"
  Abstract: 3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however that only one image cannot provide enough information for such a reconstruction. A prior knowledge that has been entertained are 3D CAD models due to its online ubiquity. A fundamental question is how to compactly represent millions of CAD models while allowing generalization to new unseen objects with fine-scaled geometry. We introduce an approach to compactly represent a 3D mesh. Our method first selects a 3D model from a graph structure by using a novel free-form deformation FFD 3D-2D registration, and then the selected 3D model is refined to best fit the image silhouette. We perform a comprehensive quantitative and qualitative analysis that demonstrates impressive dense and realistic 3D reconstruction from single images.

Connected through hyperedge HE-1 (induced by source paper "what do single view 3d reconstruction networks learn"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "open3d a modern library for 3d data processing"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "megadepth learning single view depth prediction from internet photos"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-2 (induced by source paper "learning free form deformations for 3d object reconstruction"):
  with members:
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"

Connected through hyperedge HE-3 (induced by source paper "image2mesh a learning framework for single image 3d reconstruction"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"

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)