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: "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"
  Abstract: Abstract   This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures.

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"
    - "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds"
    - "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"
    - "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"
    - "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds"

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"
    - "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds"

Connected through hyperedge HE-4 (induced by source paper "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds"):
  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"

Connected through hyperedge HE-5 (induced by source paper "a scalable approach for tree segmentation within small footprint airborne lidar data"):
  with members:
    - "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters"

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)