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: "render4completion synthesizing multi view depth maps for 3d shape completion"
  Abstract: We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or point clouds, we propose to use multi-view depth maps from a set of fixed viewing angles as our shape representation. This allows us to be free of the limitations of memory for volumetric representations and point clouds by casting shape completion into an image-to-image translation problem. Specifically, we render depth maps of the incomplete shape from a fixed set of viewpoints, and perform depth map completion in each view. Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view. This enables MVCN to leverage more information from different depth views to achieve high accuracy in single depth view completion and keep the consistency among the completed depth images in different views. Benefited by the multi-view representation and the novel network structure, MVCN significantly improves the accuracy of 3D shape completion in large-scale benchmarks compared to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "what do single view 3d reconstruction networks learn"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"

Connected through hyperedge HE-2 (induced by source paper "lrc net learning discriminative features on point clouds by encodinglocal region contexts"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-3 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "3d aware scene manipulation via inverse graphics"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "ec net an edge aware point set consolidation network"

Connected through hyperedge HE-4 (induced by source paper "learning to generate dense point clouds with textures on multiple categories"):
  with members:
    - "3d shape completion with multi view consistent inference"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "inferring point clouds from single monocular images by depth intermediation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-5 (induced by source paper "3d shape completion with multi view consistent inference"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "point convolutional neural networks by extension operators"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pcn point completion network"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "deformable shape completion with graph convolutional autoencoders"

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)