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 "drone based rgbt vehicle detection and counting a challenge"):
  Abstract: Camera-equipped drones can capture targets on the ground from a wider field of view than static cameras or moving sensors over the ground. In this paper we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform. The images in the benchmark were captured over various urban areas, which include different types of urban roads, residential areas, parking lots, highways, etc., from day to night. Specifically, DroneVehicle consists of 15,532 pairs of images, i.e., RGB images and infrared images with rich annotations, including oriented object bounding boxes, object categories, etc. With intensive amount of effort, our benchmark has 441,642 annotated instances in 31,064 images. As a large-scale dataset with both RGB and thermal infrared (RGBT) images, the benchmark enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. In particular, we design two popular tasks with the benchmark, including object detection and object counting. All these tasks are extremely challenging in the proposed dataset due to factors such as illumination, occlusion, and scale variations. We hope the benchmark largely boost the research and development in visual analysis on drone platforms. The DroneVehicle dataset can be download from https://github.com/VisDrone/DroneVehicle.

Includes members:
  Member "nwpu crowd a large scale benchmark for crowd counting":
    also connected through other hyperedges:
      (none)
  Member "locality constrained spatial transformer network for video crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "perspective guided convolution networks for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "learning from synthetic data for crowd counting in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "mrcnet crowd counting and density map estimation in aerial and ground imagery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "composition loss for counting density map estimation and localization in dense crowds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "vision meets drones a challenge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "the unmanned aerial vehicle benchmark object detection and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")

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)