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 "keyfilter aware real time uav object tracking"):
  Abstract: Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired background distraction. Existing frame-by-frame context learning strategies for repressing background distraction nevertheless lower the tracking speed. Inspired by keyframe-based simultaneous localization and mapping, keyfilter is proposed in visual tracking for the first time, in order to handle the above issues efficiently and effectively. Keyfilters generated by periodically selected keyframes learn the context intermittently and are used to restrain the learning of filters, so that 1) context awareness can be transmitted to all the filters via keyfilter restriction, and 2) filter corruption can be repressed. Compared to the state-of-the-art results, our tracker performs better on two challenging benchmarks, with enough speed for UAV real-time applications.

Includes members:
  Member "can a robot become a movie director learning artistic principles for aerial cinematography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making")
  Member "learning aberrance repressed correlation filters for real time uav tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training set distillation for real time uav object tracking")
  Member "unsupervised deep tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmented memory for correlation filters in real time uav tracking")
  Member "learning spatial temporal regularized correlation filters for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training set distillation for real time uav object tracking")
  Member "kernel cross correlator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph optimization approach to range based localization")
  Member "integrating boundary and center correlation filters for visual tracking with aspect ratio variation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised deep tracking")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")

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)