human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fclt a fully correlational long term tracker"
  Abstract: We propose FCLT - a fully-correlational long-term tracker. The two main components of FCLT are a short-term tracker which localizes the target in each frame and a detector which re-detects the target when it is lost. Both the short-term tracker and the detector are based on correlation filters. The detector exploits properties of the recent constrained filter learning and is able to re-detect the target in the whole image efficiently. A failure detection mechanism based on correlation response quality is proposed. The FCLT is tested on recent short-term and long-term benchmarks. It achieves state-of-the-art results on the short-term benchmarks and it outperforms the current best-performing tracker on the long-term benchmark by over 18%.

Connected through hyperedge HE-1 (induced by source paper "exploring 3 r s of long term tracking re detection recovery and reliability"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "yolov3 an incremental improvement"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-2 (induced by source paper "cdtb a color and depth visual object tracking dataset and benchmark"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "depth masked discriminative correlation filter"
    - "object tracking by reconstruction with view specific discriminative correlation filters"
    - "now you see me evaluating performance in long term visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "learning regression and verification networks for long term visual tracking"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "learning regression and verification networks for long term visual tracking"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "a twofold siamese network for real time object tracking"
    - "correlation tracking via joint discrimination and reliability learning"
    - "now you see me evaluating performance in long term visual tracking"
    - "random erasing data augmentation"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "object tracking by reconstruction with view specific discriminative correlation filters"):
  with members:
    - "depth masked discriminative correlation filter"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "visual object tracking performance measures revisited"
    - "fully convolutional siamese networks for object tracking"
    - "discriminative scale space tracking"
    - "high speed tracking with kernelized correlation filters"
    - "correlation filters with limited boundaries"

Connected through hyperedge HE-5 (induced by source paper "effective occlusion handling for fast correlation filter based trackers"):
  with members:
    - "unveiling the power of deep tracking"

Question: Please tell me which class the center node 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)