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: "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"
  Abstract: Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.

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:
    - "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"
    - "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:
    - "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"
    - "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"

Connected through hyperedge HE-3 (induced by source paper "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar 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)