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: "deep motion features for visual tracking"
  Abstract: Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. #R##N#This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.

Connected through hyperedge HE-1 (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"
    - "now you see me evaluating performance in long term visual tracking"
    - "learning regression and verification networks for long term visual tracking"

Connected through hyperedge HE-2 (induced by source paper "remove cosine window from correlation filter based visual trackers when and how"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "unveiling the power of deep tracking"
    - "correlation tracking via joint discrimination and reliability learning"
    - "vital visual tracking via adversarial learning"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-4 (induced by source paper "handcrafted and deep trackers a review of recent object tracking approaches"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "vital visual tracking via adversarial learning"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "correlation filter selection for visual tracking using reinforcement learning"):
  with members:
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "end to end active object tracking via reinforcement learning"
    - "large margin object tracking with circulant feature maps"
    - "tree structured reinforcement learning for sequential object localization"
    - "deep reinforcement learning for visual object tracking in videos"

Connected through hyperedge HE-6 (induced by source paper "large margin structured convolution operator for thermal infrared object tracking"):
  with members:
    - "large margin object tracking with circulant feature maps"
    - "a novel performance evaluation methodology for single target trackers"
    - "eco efficient convolution operators for tracking"
    - "geometric hypergraph learning for visual tracking"
    - "learning support correlation filters for visual tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "differentiating objects by motion joint detection and tracking of small flying objects"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "flow guided feature aggregation for video object detection"
    - "perceptual generative adversarial networks for small object detection"
    - "detect to track and track to detect"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "end to end flow correlation tracking with spatial temporal attention"):
  with members:
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "flow guided feature aggregation for video object detection"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "squeeze and excitation networks"
    - "crest convolutional residual learning for visual tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object 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)