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: "fine to coarse global registration of rgb d scans"
  Abstract: RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a "fine-to-coarse" global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "lcd learned cross domain descriptors for 2d 3d matching"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "learning a local feature descriptor for 3d lidar scans"

Connected through hyperedge HE-3 (induced by source paper "extreme relative pose network under hybrid representations"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "extreme relative pose estimation for rgb d scans via scene completion"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning an effective equivariant 3d descriptor without supervision"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "sampling matters in deep embedding learning"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning compact geometric features"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "minimal solvers for mini loop closures in 3d multi scan alignment"):
  with members:
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "tangent convolutions for dense prediction in 3d"
    - "learning compact geometric features"
    - "semantic visual localization"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "floorplan jigsaw jointly estimating scene layout and aligning partial scans"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "exploiting 2d floorplan for building scale panorama rgbd alignment"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-7 (induced by source paper "efficient plane based optimization of geometry and texture for indoor rgb d reconstruction"):
  with members:
    - "plane based optimization of geometry and texture for rgb d reconstruction of indoor scenes"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "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"
    - "surface approximation via asymptotic optimal geometric partition"

Connected through hyperedge HE-8 (induced by source paper "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"):
  with members:
    - "dynamic graph convolutional networks"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "scores shape composition with recursive substructure priors"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic3d net a new large scale point cloud classification benchmark"

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)