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: "lime a method for low light image enhancement"
  Abstract: When one captures images in low-light conditions, the images often suffer from low visibility. This poor quality may significantly degrade the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a very simple and effective method, named as LIME, to enhance low-light images. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G and B channels. Further, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging real-world low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "multi frame gan image enhancement for stereo visual odometry in low light"):
  with members:
    - "video to video synthesis"
    - "reconet real time coherent video style transfer network"
    - "learning linear transformations for fast arbitrary style transfer"
    - "night to day image translation for retrieval based localization"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "drit diverse image to image translation via disentangled representations"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-2 (induced by source paper "tracking system of mine patrol robot for low illumination environment"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "a survey of appearance models in visual object 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)