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 "tracking system of mine patrol robot for low illumination environment"):
  Abstract: Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, tracking in low-illumination environments is a challenging problem, not yet successfully addressed in the literature. In this work, we tackle the problems by introducing Low-Illumination Long-term Correlation Tracker (LLCT). First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard PCA to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, update memory templates with interval updates, then re-match existing and initial templates every few frames to maintain template accuracy. The extensive experiments on popular Object Tracking Benchmark OTB-50 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance. In addition, the proposed approach can be integrated easily in robot system and the running speed performed well. The experimental results show that the novel tracker performance in low-illumination environment is better than that of general trackers.

Includes members:
  Member "adaptive correlation filters with long term and short term memory for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "mdnet a semantically and visually interpretable medical image diagnosis network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clara clinical report auto completion")
  Member "large margin object tracking with circulant feature maps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "learning background aware correlation filters 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 "eco efficient convolution operators for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "beyond correlation filters learning continuous convolution operators 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 "lime a method for low light image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi frame gan image enhancement for stereo visual odometry in low light")

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)