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: "incremental reconstruction of urban environments by edge points delaunay triangulation"
  Abstract: Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping. When computational power represents a limited resource and, a detailed map is not the primary goal, the reconstruction can be performed incrementally, from a monocular video, carving a 3D Delaunay triangulation of sparse points; this allows online incremental mapping for tasks such as traversability analysis or obstacle avoidance. To exploit the sharp edges of urban landscape, we propose to use a Delaunay triangulation of Edge-Points, which are the 3D points corresponding to image edges. These points constrain the edges of the 3D Delaunay triangulation to real-world edges. Besides the use of the Edge-Points, a second contribution of this paper is the Inverse Cone Heuristic that preemptively avoids the creation of artifacts in the reconstructed manifold surface. We force the reconstruction of a manifold surface since it makes it possible to apply computer graphics or photometric refinement algorithms to the output mesh. We evaluated our approach on four real sequences of the public available KITTI dataset by comparing the incremental reconstruction against Velodyne measurements.

Connected through hyperedge HE-1 (induced by source paper "creating navigable space from sparse noisy map points"):
  with members:
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topomap topological mapping and navigation based on visual slam maps"
    - "voxblox building 3d signed distance fields for planning"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-2 (induced by source paper "dense 3d visual mapping via semantic simplification"):
  with members:
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"

Connected through hyperedge HE-3 (induced by source paper "a data driven prior on facet orientation for semantic mesh labeling"):
  with members:
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "efficient 2d and 3d facade segmentation using auto context"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "reseg a recurrent neural network based model for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi view stereo 3d edge reconstruction"):
  with members:
    - "detail preserving and content aware variational multi view stereo reconstruction"

Connected through hyperedge HE-5 (induced by source paper "predicting the next best view for 3d mesh refinement"):
  with members:
    - "real time cpu based large scale 3d mesh reconstruction"

Connected through hyperedge HE-6 (induced by source paper "mesh based 3d textured urban mapping"):
  with members:
    - "detail preserving and content aware variational multi view stereo reconstruction"
    - "automatic 3d reconstruction of manifold meshes via delaunay triangulation and mesh sweeping"
    - "robust moving objects detection in lidar data exploiting visual cues"
    - "efficient moving point handling for incremental 3d manifold reconstruction"

Connected through hyperedge HE-7 (induced by source paper "multi view stereo with single view semantic mesh refinement"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "automatic 3d reconstruction of manifold meshes via delaunay triangulation and mesh sweeping"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "automatic 3d reconstruction of manifold meshes via delaunay triangulation and mesh sweeping"):
  with members:
    - "detail preserving and content aware variational multi view stereo reconstruction"
    - "efficient moving point handling for incremental 3d manifold reconstruction"

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)