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: "learning and matching multi view descriptors for registration of point clouds"
  Abstract: Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods.

Connected through hyperedge HE-1 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "d3feat joint learning of dense detection and description of 3d local features"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "kpconv flexible and deformable convolution for point clouds"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "lf net learning local features from images"

Connected through hyperedge HE-3 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-4 (induced by source paper "learning two view correspondences and geometry using order aware network"):
  with members:
    - "contextdesc local descriptor augmentation with cross modality context"
    - "efficient semantic scene completion network with spatial group convolution"
    - "lf net learning local features from images"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "neural nearest neighbors networks"
    - "self improving visual odometry"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-5 (induced by source paper "poirot a rotation invariant omni directional pointnet"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "a cnn for homogneous riemannian manifolds with applications to neuroimaging"
    - "splatnet sparse lattice networks for point cloud processing"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-6 (induced by source paper "spatial transformer for 3d point clouds"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "splatnet sparse lattice networks for point cloud processing"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "so net self organizing network for point cloud analysis"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-7 (induced by source paper "matchable image retrieval by learning from surface reconstruction"):
  with members:
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "end to end learning of deep visual representations for image retrieval"
    - "learning deep embeddings with histogram loss"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "tensorflow a system for large scale machine learning"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)