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: "visual tracking via dynamic graph learning"
  Abstract: Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by the inclusion of background clutter. To handle this problem, we learn a patch-based graph representation for visual tracking. The tracked object is modeled by with a graph by taking a set of non-overlapping image patches as nodes, in which the weight of each node indicates how likely it belongs to the foreground and edges are weighted for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learned and applied in object tracking and model updating. During the tracking process, the proposed algorithm performs three main steps in each frame. First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box. Second, the graph is optimized to refine the patch weights by using a novel alternating direction method of multipliers. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is predicted by maximizing the classification score in the structured support vector machine. Extensive experiments show that the proposed tracking algorithm performs well against the state-of-the-art methods on large-scale benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "edge guided non local fully convolutional network for salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "edge preserving and multi scale contextual neural network for salient object detection"
    - "deep edge aware saliency detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"

Connected through hyperedge HE-2 (induced by source paper "rgb t image saliency detection via collaborative graph learning"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "rgb t object tracking benchmark and baseline"
    - "robust visual tracking via hierarchical convolutional features"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "deep residual learning for image recognition"
    - "the secrets of salient object segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning compact target oriented feature representations for visual tracking"):
  with members:
    - "rgb t object tracking benchmark and baseline"
    - "robust visual tracking via hierarchical convolutional features"
    - "fully convolutional siamese networks for object tracking"
    - "discriminative scale space tracking"
    - "an overview of gradient descent optimization algorithms"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "staple complementary learners for real time tracking"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "rgb t object tracking benchmark and baseline"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "a twofold siamese network for real time object tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "dynamic label graph matching for unsupervised video re identification"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "learning multi domain convolutional neural networks for visual 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)