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: "3dmatch learning local geometric descriptors from rgb d reconstructions"
  Abstract: Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at this http URL

Connected through hyperedge HE-1 (induced by source paper "scan2mesh from unstructured range scans to 3d meshes"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "deformable shape completion with graph convolutional autoencoders"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-2 (induced by source paper "object aware guidance for autonomous scene reconstruction"):
  with members:
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "motion planning strategies for autonomously mapping 3d structures"
    - "3dmatch learning the matching of local 3d geometry in range scans"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"

Connected through hyperedge HE-3 (induced by source paper "learning local shape descriptors from part correspondences with multi view convolutional networks"):
  with members:
    - "zero shot learning the good the bad and the ugly"
    - "lift learned invariant feature transform"
    - "3d shape segmentation with projective convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-4 (induced by source paper "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"):
  with members:
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "global hypothesis generation for 6d object pose estimation"
    - "perch perception via search for multi object recognition and localization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "single image 3d interpreter network"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"
    - "a dual source approach for 3d pose estimation from a single image"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-6 (induced by source paper "shape completion using 3d encoder predictor cnns and shape synthesis"):
  with members:
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "vconv dae deep volumetric shape learning without object labels"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "a large dataset of object scans"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"

Connected through hyperedge HE-7 (induced by source paper "fine to coarse global registration of rgb d scans"):
  with members:
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "convolutional neural network based place recognition"
    - "contextually guided semantic labeling and search for 3d point clouds"

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)