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 "learning matchable colorspace transformations for long term metric visual localization"):
  Abstract: Long-term metric localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems in the presence of appearance change caused by lighting, weather or seasonal variations. While experience-based mapping has proven to be an effective technique for enabling visual localization across appearance change, the number of experiences required for reliable long-term localization can be large, and methods for reducing the necessary number of experiences are desired. Taking inspiration from physics-based models of color constancy, we propose a method for learning a nonlinear mapping from RGB to grayscale colorspaces that maximizes the number of feature matches for images captured under varying lighting and weather conditions. Our key insight is that useful image transformations can be learned by approximating conventional non-differentiable localization pipelines with a differentiable learned model that can predict a convenient measure of localization quality, such as the number of feature matches, for a given pair of images. Moreover, we find that the generality of appearance-robust RGB-to-grayscale mappings can be improved by incorporating a learned low-dimensional context feature computed for a specific image pair. Using synthetic and real-world datasets, we show that our method substantially improves feature matching across day-night cycles and presents a viable strategy for significantly improving the efficiency of experience-based visual localization.

Includes members:
  Member "night to day image translation for retrieval based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised learning methods for visual place recognition in discretely and continuously changing environments")
  Member "adversarial training for adverse conditions robust metric localisation using appearance transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rsl net localising in satellite images from a radar on the ground")
  Member "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "learning based image enhancement for visual odometry in challenging hdr environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "dpc net deep pose correction for visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "addressing challenging place recognition tasks using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable place recognition under appearance change for autonomous driving")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)