human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rethinking convolutional features in correlation filter based tracking"):
  Abstract: Both accuracy and efficiency are of significant importance to the task of visual object tracking. In recent years, as the surge of deep learning, Deep Convolutional NeuralNetwork (DCNN) becomes a very popular choice among the tracking community. However, due to the high computational complexity, end-to-end visual object trackers can hardly achieve an acceptable inference time and therefore can difficult to be utilized in many real-world applications. In this paper, we revisit a hierarchical deep feature-based visual tracker and found that both the performance and efficiency of the deep tracker are limited by the poor feature quality. Therefore, we propose a feature selection module to select more discriminative features for the trackers. After removing redundant features, our proposed tracker achieves significant improvements in both performance and efficiency. Finally, comparisons with state-of-the-art trackers are provided.

Includes members:
  Member "context aware deep feature compression for high speed visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "a twofold siamese network for real time object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "deep attentive tracking via reciprocative learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving")
  Member "learning spatial aware regressions for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "dual deep network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "robust and real time deep tracking via multi scale domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online visual robot tracking and identification using deep lstm networks")
  Member "a novel performance evaluation methodology for single target trackers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "sanet structure aware network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")

Question: Please tell me which class the source paper 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)