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 "high performance visual object tracking with unified convolutional networks"):
  Abstract: Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in different tasks and individual components in tracking systems are learned separately, thus the achieved tracking performance may be suboptimal. Besides, most of these trackers are not designed towards real-time applications because of their time-consuming feature extraction and complex optimization details. In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT). Specifically, the UCT treats feature extractor and tracking process both as convolution operation and trains them jointly, which enables learned CNN features are tightly coupled with tracking process. During online tracking, an efficient model updating method is proposed by introducing peak-versus-noise ratio (PNR) criterion, and scale changes are handled efficiently by incorporating a scale branch into network. Experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016. Our method achieves leading performance on these benchmarks while maintaining beyond real-time speed.

Includes members:
  Member "exploiting offset guided network for pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fastpose towards real time pose estimation and tracking via scale normalized multi task networks")
  Member "state aware re identification feature for multi target multi camera tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "fastpose towards real time pose estimation and tracking via scale normalized multi task networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "15 keypoints is all you need")
  Member "optical flow based real time moving object detection in unconstrained scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "human following for wheeled robot with monocular pan tilt camera")
  Member "optical flow based online moving foreground analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "motion control on bionic eyes a comprehensive review")
  Member "action machine rethinking action recognition in trimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mimetics towards understanding human actions out of context")
  Member "distractor aware siamese networks for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "an efficient optical flow based motion detection method for non stationary scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "moving objects detection with a moving camera a comprehensive review")

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)