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: "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds"
  Abstract: Abstract   Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest – a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types.

Connected through hyperedge HE-1 (induced by source paper "deep learning for conifer deciduous classification of airborne lidar 3d point clouds representing individual trees"):
  with members:
    - "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"
    - "a scalable approach for tree segmentation within small footprint airborne lidar data"
    - "3d shape segmentation with projective convolutional networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "volumetric and multi view cnns for object classification on 3d data"
    - "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "remote sensing of forests using discrete return airborne lidar"):
  with members:
    - "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"
    - "a scalable approach for tree segmentation within small footprint airborne lidar data"
    - "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"

Connected through hyperedge HE-3 (induced by source paper "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"):
  with members:
    - "a scalable approach for tree segmentation within small footprint airborne lidar data"
    - "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"

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)