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 randomly moving objects on edge box proposals"
  Abstract: Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra low-frame-rate videos.

Connected through hyperedge HE-1 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"
    - "deep reinforcement learning for visual object tracking in videos"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "learning feed forward one shot learners"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-2 (induced by source paper "siamvgg visual tracking using deeper siamese networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "robust visual tracking using multi frame multi feature joint modeling"):
  with members:
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "visual tracking via reliable memories"
    - "discriminative scale space tracking"
    - "an experimental survey on correlation filter based tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "online object tracking with proposal selection"
    - "a survey of appearance models in visual object tracking"

Connected through hyperedge HE-4 (induced by source paper "non rigid object tracking via deep multi scale spatial temporal discriminative saliency maps"):
  with members:
    - "salient object detection a survey"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "visual saliency based on multiscale deep features"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "learning policies for adaptive tracking with deep feature cascades"):
  with members:
    - "deep reinforcement learning for visual object tracking in videos"
    - "eco efficient convolution operators for tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "learning spatially regularized correlation filters for visual tracking"
    - "transferring rich feature hierarchies for robust visual tracking"

Connected through hyperedge HE-6 (induced by source paper "dcco towards deformable continuous convolution operators"):
  with members:
    - "deformable parts correlation filters for robust visual tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "modeling and propagating cnns in a tree structure for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "fully convolutional siamese networks for object tracking"):
  with members:
    - "siamese instance search for tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "once for all a two flow convolutional neural network for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "first step toward model free anonymous object tracking with recurrent neural networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "ratm recurrent attentive tracking model"

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)