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: "planar object tracking in the wild a benchmark"
  Abstract: Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.

Connected through hyperedge HE-1 (induced by source paper "multi drone based single object tracking with agent sharing network"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-2 (induced by source paper "human following for wheeled robot with monocular pan tilt camera"):
  with members:
    - "detection and tracking of small objects in sparse 3d laser range data"
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "action machine rethinking action recognition in trimmed videos"
    - "attention guided unified network for panoptic segmentation"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-3 (induced by source paper "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  with members:
    - "pose estimator and tracker using temporal flow maps for limbs"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "a fusion approach for multi frame optical flow estimation"

Connected through hyperedge HE-4 (induced by source paper "visual coin tracking tracking of planar double sided objects"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "unveiling the power of deep tracking"
    - "multi hierarchical independent correlation filters for visual tracking"
    - "youtube vos sequence to sequence video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "learning regression and verification networks for long term visual tracking"
    - "continual occlusions and optical flow estimation"

Connected through hyperedge HE-5 (induced by source paper "improving human annotation in single 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"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "high speed tracking with kernelized correlation filters"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-6 (induced by source paper "instant motion tracking and its applications to augmented reality"):
  with members:
    - "robust estimation of similarity transformation for visual object tracking"

Connected through hyperedge HE-7 (induced by source paper "siamese learning visual tracking a survey"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "a twofold siamese network for real time object tracking"
    - "good features to correlate for visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "end to end representation learning for correlation filter based tracking"
    - "focal loss for dense object detection"
    - "hierarchical attentive recurrent tracking"
    - "multi task self supervised visual learning"

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)