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: "now you see me evaluating performance in long term visual tracking"
  Abstract: We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances. We perform an extensive evaluation of six long-term and nine short-term state-of-the-art trackers, using new performance measures, suitable for evaluating long-term tracking - tracking precision, recall and F-score. The evaluation shows that a good model update strategy and the capability of image-wide re-detection are critical for long-term tracking performance. We integrated the methodology in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate the development of long-term trackers.

Connected through hyperedge HE-1 (induced by source paper "siamman siamese motion aware network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "domainsiam domain aware siamese network for visual object tracking"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "fast visual object tracking with rotated bounding boxes"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"

Connected through hyperedge HE-2 (induced by source paper "skimming perusal tracking a framework for real time and robust long term tracking"):
  with members:
    - "gradnet gradient guided network for visual object tracking"
    - "real time mdnet"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "correlation tracking via joint discrimination and reliability learning"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-3 (induced by source paper "flow guided short term trackers with cascade detection for long term tracking"):
  with members:
    - "region proposal by guided anchoring"
    - "vision meets drones a challenge"
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "yolov3 an incremental improvement"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "learning regression and verification networks for long term visual tracking"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation methodology for long term visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "long term tracking in the wild a benchmark"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "tracking for half an hour"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "long term visual object tracking benchmark"
    - "need for speed a benchmark for higher frame rate object tracking"

Connected through hyperedge HE-5 (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-6 (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"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "learning regression and verification networks for long term visual tracking"
    - "fclt a fully correlational long term tracker"
    - "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-7 (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"
    - "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"
    - "fclt a fully correlational long term tracker"

Connected through hyperedge HE-8 (induced by source paper "long term tracking in the wild a benchmark"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "tracking for half an hour"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "learning background aware correlation filters for visual tracking"
    - "long term visual object tracking benchmark"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"
    - "a novel performance evaluation methodology for single target trackers"

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)