human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning for conifer deciduous classification of airborne lidar 3d point clouds representing individual trees"):
  Abstract: The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees from airborne LiDAR data. To enable efficient processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSMx4) and a set of four 2D views (4x2D). A training dataset of labeled tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples of augmented data (180 rotational variations per instance). The 4x2D representation yielded similar classification accuracies to the DSMx4 representation (~82% coniferous and ~90% deciduous) while converging faster. The data augmentation improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvements. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. Automatic derivation of optimal features via deep learning provide the opportunity for remarkable improvements in prediction tasks where captured data are not friendly to human visual system - likely yielding sub-optimal human-designed features.

Includes members:
  Member "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "remote sensing of forests using discrete return airborne lidar")
  Member "a scalable approach for tree segmentation within small footprint airborne lidar data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "remote sensing of forests using discrete return airborne lidar")
  Member "vertical stratification of forest canopy for segmentation of understory trees within small footprint airborne lidar point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "remote sensing of forests using discrete return airborne lidar")
  Member "3d shape segmentation with projective convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "octnet learning deep 3d representations at high resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "volumetric and multi view cnns for object classification on 3d data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "remote sensing of forests using discrete return airborne lidar")
  Member "improving computer aided detection using convolutional neural networks and random view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")

Question: Please tell me which class the source paper 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.LG(Machine Learning)