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 "efficient condition based representations for long term visual localization"):
  Abstract: We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening when capturing conditions change throughout the day or across seasons. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel retrieval-based method introduces condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We compare our approach to previous localization methods on very recent challenging benchmarks, and observe that our method outperforms them by a large margin in case of day-night variation, where repeatable feature points cannot be identified or matched.

Includes members:
  Member "scene coordinate and correspondence learning for image based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization")
  Member "night to day image translation for retrieval based localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised learning methods for visual place recognition in discretely and continuously changing environments")
  Member "revisiting oxford and paris large scale image retrieval benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unifying deep local and global features for image search")
  Member "inloc indoor visual localization with dense matching and view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "semantic visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "segmap segment based mapping and localization using data driven descriptors")
  Member "fine tuning cnn image retrieval with no human annotation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

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)