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 "counting and segmenting sorghum heads"):
  Abstract: Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network architecture. We also propose a novel instance segmentation algorithm using the estimated density map, to identify the individual panicles in the presence of occlusion. With real Sorghum aerial images, we obtain a mean absolute error (MAE) of 1.06 for counting which is better than using well-known crowd counting approaches such as CCNN, MCNN and CSRNet models. The instance segmentation model also produces respectable results which will be ultimately useful in reducing the manual annotation workload for future data.

Includes members:
  Member "crowd counting with decomposed uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate query service on autonomous iot cameras")
  Member "embedded cnn based vehicle classification and counting in non laned road traffic":
    also connected through other hyperedges:
      (none)
  Member "instance segmentation by deep coloring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "adaptive scenario discovery for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "a ccnn adaptive ccnn for density estimation and crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "maskrnn instance level video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "using deep learning for segmentation and counting within microscopy data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "segmentation of microscopy data for finding nuclei in divergent images")
  Member "csrnet dilated convolutional neural networks for understanding the highly congested scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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.CV(Computer Vision and Pattern Recognition)