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 detection using depth maps"):
  Abstract: Obstacle avoidance is a key feature for safe Unmanned Aerial Vehicle (UAV) navigation. While solutions have been proposed for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are hard to implement due to the detection range and field-of-view (FOV) requirements, as well as the constraints for integrating such systems on-board small UAVs. In this work, a dataset of 6k synthetic depth maps of drones has been generated and used to train a state-of-the-art deep learning-based drone detection model. While many sensing technologies can only provide relative altitude and azimuth of an obstacle, our depth map-based approach enables full 3D localization of the obstacle. This is extremely useful for collision avoidance, as 3D localization of detected drones is key to perform efficient collision-free path planning. The proposed detection technique has been validated in several real depth map sequences, with multiple types of drones flying at up to 2 m/s, achieving an average precision of 98.7%, an average recall of 74.7% and a record detection range of 9.5 meters.

Includes members:
  Member "vision based real time aerial object localization and tracking for uav sensing system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reconstruction of 3d flight trajectories from ad hoc camera networks")
  Member "airsim high fidelity visual and physical simulation for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "using deep networks for drone detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "flying objects detection from a single moving camera":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a novel technique for rejecting non aircraft artefacts in above horizon vision based aircraft 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.RO(Robotics)