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: "dual deep network for visual tracking"
  Abstract: Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To highlight geometric contours of the target, we integrate the hierarchical feature maps with an edge detector as the coarse prior maps to further embed local details around the target. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance, which serves as a regularization to enforce the dual network to focus on target object. The proposed dual network is updated online in a unique manner based on the observation, that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background. It is also found that for a target object, the prior maps can help further enhance performance by passing message into the output maps of the dual network. Therefore, an independent component analysis with reference algorithm is employed to extract target context using prior maps as guidance. Online tracking is conducted by maximizing the posterior estimate on the final maps with stochastic and periodic update. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-3 (induced by source paper "visual tracking via dynamic memory networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "mavot memory augmented video object tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-4 (induced by source paper "rethinking convolutional features in correlation filter based tracking"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "deep attentive tracking via reciprocative learning"
    - "learning spatial aware regressions for visual tracking"
    - "robust and real time deep tracking via multi scale domain adaptation"
    - "a novel performance evaluation methodology for single target trackers"
    - "sanet structure aware network for visual tracking"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-5 (induced by source paper "parallel tracking and verifying"):
  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"
    - "sanet structure aware network for visual tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "discriminative scale space tracking"
    - "modeling and propagating cnns in a tree structure for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-6 (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-7 (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"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-8 (induced by source paper "siamese cascaded region proposal networks for real time visual tracking"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"
    - "towards a better match in siamese network based visual object tracker"
    - "vital visual tracking via adversarial learning"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

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)