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: "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"
  Abstract: Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy is limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these problems, we propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching. The methods of this proposed approach have linear time complexity with regard to the number of images, can explicitly handle low overlap using multi-view images and can be directly injected into off-the-shelf structure-from-motion (SfM) and multi-view stereo (MVS) solutions. First, aerial and ground images are reconstructed separately and initially co-registered through weak georeferencing data. Second, aerial models are rendered to the initial ground views, in which the color, depth and normal images are obtained. Then, the synthesized color images and the corresponding ground images are matched by comparing the descriptors, filtered by local geometrical information, and then propagated to the aerial views using depth images and patch-based matching. Experimental evaluations using various datasets confirm the superior performance of the proposed methods in aerial-ground image matching. In addition, incorporation of the existing SfM and MVS solutions into these methods enables more complete and accurate models to be directly obtained.

Connected through hyperedge HE-1 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception"):
  with members:
    - "bridging the domain gap for ground to aerial image matching"
    - "lending orientation to neural networks for cross view geo localization"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "cross view image matching for geo localization in urban environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "semantic cross view matching"
    - "wide area image geolocalization with aerial reference imagery"
    - "faster r cnn towards real time object detection with region proposal networks"

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)