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 "scalable place recognition under appearance change for autonomous driving"):
  Abstract: A major challenge in place recognition for autonomous driving is to be robust against appearance changes due to short-term (e.g., weather, lighting) and long-term (seasons, vegetation growth, etc.) environmental variations. A promising solution is to continuously accumulate images to maintain an adequate sample of the conditions and incorporate new changes into the place recognition decision. However, this demands a place recognition technique that is scalable on an ever growing dataset. To this end, we propose a novel place recognition technique that can be efficiently retrained and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from visible growth in computational cost. Underpinning our method is a novel temporal image matching technique based on Hidden Markov Models. Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.

Includes members:
  Member "semi parametric topological memory for navigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "dels 3d deep localization and segmentation with a 3d semantic map":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving")
  Member "polysemous codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to compress and search visual data in large scale systems")
  Member "adversarial training for adverse conditions robust metric localisation using appearance transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rsl net localising in satellite images from a radar on the ground")
  Member "vidloc a deep spatio temporal model for 6 dof video clip relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "benchmarking 6dof urban visual localization in changing conditions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time kinematic ground truth for the oxford robotcar dataset")
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")

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)