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 scalable approach for tree segmentation within small footprint airborne lidar data"
  Abstract: This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. A minimal bias was introduced to the number of detected trees because of trees lying across the tile boundaries, which was quantified and adjusted for. Theoretical and experimental analyses of the runtime of the approach revealed a near linear speedup. The estimated number of trees categorized by crown class and the associated error margins as well as the height distribution of the detected trees aligned well with field estimations, verifying that the distributed approach works correctly. The approach enables providing information of individual tree locations and point cloud segments for a forest-level area in a timely manner, which can be used to create detailed remotely sensed forest inventories. Although the approach was presented for tree segmentation within LiDAR point clouds, the idea can also be generalized to scale up processing other big spatial datasets. A scalable distributed approach for tree segmentation was developed and analyzed.~2 million trees in a 7440 ha forest was segmented in 2.5h using 192 cores.2% false positive trees were identified as a result of the distributed run.Estimated number of trees and the tree height distribution aligned with field data.The approach can be used to scale up processing other big spatial data.

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"
    - "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:
    - "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"
    - "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 "forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds"):
  with members:
    - "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-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 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.DC(Distributed, Parallel, and Cluster Computing)