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 "tracking of micro unmanned aerial vehicles a comparative study"):
  Abstract: Micro unmanned aerial vehicles (mUAV) became very common in recent years. As a result of their widespread usage, when they are flown by hobbyists illegally, crucial risks are imposed and such mUAVs need to be sensed by security systems. Furthermore, the sensing of mUAVs are essential for also swarm robotics research where the individuals in a flock of robots require systems to sense and localize each other for coordinated operation. In order to obtain such systems, there are studies to detect mUAVs utilizing different sensing mediums, such as vision, infrared and sound signals, and small-scale radars. However, there are still challenges that awaits to be handled in this field such as integrating tracking approaches to the vision-based detection systems to enhance accuracy and computational complexity. For this reason, in this study, we combine various tracking approaches to a vision-based mUAV detection system available in the literature, in order to evaluate different tracking approaches in terms of accuracy and as well as investigate the effect of such integration to the computational cost.

Includes members:
  Member "discriminative correlation filter tracker with channel and spatial reliability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps")
  Member "detect or track towards cost effective video object detection tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a delay metric for video object detection what average precision fails to tell")
  Member "learning to track at 100 fps with deep regression networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "high speed tracking with kernelized correlation filters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "outdoor flocking and formation flight with autonomous aerial robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning vision based flight in drone swarms by imitation")

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)