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: "tracking for half an hour"
  Abstract: Long-term tracking requires extreme stability to the multitude of model updates and robustness to the disappearance and loss of the target as such will inevitably happen. For motivation, we have taken 10 randomly selected OTB-sequences, doubled each by attaching a reversed version and repeated each double sequence 20 times. On most of these repetitive videos, the best current tracker performs worse on each loop. This illustrates the difference between optimization for short-term versus long-term tracking. In a long-term tracker a combined global and local search strategy is beneficial, allowing for recovery from failures and disappearance. Most importantly, the proposed tracker also employs cautious updating, guided by self-quality assessment. The proposed tracker is still among the best on the 20-sec OTB-videos while achieving state-of-the-art on the 100-sec UAV20L benchmark. On 10 new half-an-hour videos with city bicycling, sport games etc, the proposed tracker outperforms others by a large margin where the 2010 TLD tracker comes second.

Connected through hyperedge HE-1 (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"
    - "now you see me evaluating performance in long term visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "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-2 (induced by source paper "real time tracker with fast recovery from target loss"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "end to end representation learning for correlation filter based tracking"
    - "deep reinforcement learning for visual object tracking in videos"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-3 (induced by source paper "intra frame object tracking by deblatting"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "high speed tracking with multi kernel correlation filters"
    - "learning blind motion deblurring"
    - "the world of fast moving objects"
    - "long term visual object tracking benchmark"
    - "a novel performance evaluation methodology for single target trackers"
    - "performance measures and a data set for multi target multi camera tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "non causal tracking by deblatting"):
  with members:
    - "intra frame object tracking by deblatting"
    - "features for multi target multi camera tracking and re identification"
    - "high speed tracking with multi kernel correlation filters"
    - "the world of fast moving objects"
    - "long term visual object tracking benchmark"

Connected through hyperedge HE-5 (induced by source paper "siamrpn evolution of siamese visual tracking with very deep networks"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "an in depth analysis of visual tracking with siamese neural networks"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-6 (induced by source paper "long term tracking in the wild a benchmark"):
  with members:
    - "now you see me evaluating performance in long term visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "now you see me evaluating performance in long term visual tracking"):
  with members:
    - "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"
    - "a novel performance evaluation methodology for single target trackers"
    - "beyond standard benchmarks parameterizing performance evaluation in visual object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-8 (induced by source paper "hide and seek tracker real time recovery from target loss"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "deep reinforcement learning for visual object tracking in videos"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "two stream convolutional networks for action recognition in videos"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)