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: "structure from motion using dense cnn features with keypoint relocalization"
  Abstract: Structure from Motion (SfM) using imagery that involves extreme appearance changes is yet a challenging task due to a loss of feature repeatability. Using feature correspondences obtained by matching densely extracted convolutional neural network (CNN) features significantly improves the SfM reconstruction capability. However, the reconstruction accuracy is limited by the spatial resolution of the extracted CNN features which is not even pixel-level accuracy in the existing approach. Providing dense feature matches with precise keypoint positions is not trivial because of memory limitation and computational burden of dense features. To achieve accurate SfM reconstruction with highly repeatable dense features, we propose an SfM pipeline that uses dense CNN features with relocalization of keypoint position that can efficiently and accurately provide pixel-level feature correspondences. Then, we demonstrate on the Aachen Day-Night dataset that the proposed SfM using dense CNN features with the keypoint relocalization outperforms a state-of-the-art SfM (COLMAP using RootSIFT) by a large margin.

Connected through hyperedge HE-1 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "kornia an open source differentiable computer vision library for pytorch"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "universal perturbation attack against image retrieval"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "learning a local feature descriptor for 3d lidar scans"
    - "a large dataset for improving patch matching"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "an analysis of deep neural network models for practical applications"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-4 (induced by source paper "see far with tpnet a tile processor and a cnn symbiosis"):
  with members:
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "the sound of pixels"
    - "learning to see in the dark"
    - "learning deep convolutional networks for demosaicing"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "computing the stereo matching cost with a convolutional neural network"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)