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 "a review of visual trackers and analysis of its application to mobile robot"):
  Abstract: Computer vision has received a significant attention in recent year, which is one of the important parts for robots to obtain information about the external environment. Visual trackers can provide the necessary physical and environmental parameters for the mobile robot, and their performance is related to the actual application of the robot. This study provides a comprehensive survey on visual trackers. Following a brief introduction, we first analyzed the basic framework and difficulties of visual trackers. Then the structure of generative and discriminative methods is introduced, and summarized the feature descriptors, modeling methods, and learning methods which be used in tracker. Later we reviewed and evaluated the state-of-the-art progress on discriminative trackers from three directions: correlation filter, deep learning and convolutional features. Finally, we analyzed the research direction of visual tracker used in mobile robot, as well as outlined the future trends for visual tracker on mobile robot.

Includes members:
  Member "long term tracking in the wild a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "context aware deep feature compression for high speed visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "adaptive correlation filters with long term and short term memory for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving model drift for robust object tracking")
  Member "joint representation and truncated inference learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spm tracker series parallel matching for real time visual object tracking")
  Member "a twofold siamese network for real time object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "meta tracker fast and robust online adaptation for visual object trackers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aol adaptive online learning for human trajectory prediction in dynamic video scenes")
  Member "good features to correlate for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "unveiling the power of deep tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese box adaptive network for visual tracking")

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)