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 "da4ad end to end deep attention aware features aided visual localization for autonomous driving"):
  Abstract: We present a visual localization framework aided by novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving.

Includes members:
  Member "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud":
    also connected through other hyperedges:
      (none)
  Member "sparse to dense hypercolumn matching for long term visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained segmentation networks self supervised segmentation for improved long term visual localization")
  Member "understanding the limitations of cnn based absolute camera pose regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "vizard reliable visual localization for autonomous vehicles in urban outdoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mozard multi modal localization for autonomous vehicles inurban outdoor environments")
  Member "deep auxiliary learning for visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "efficient continuous time slam for 3d lidar based online mapping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "vlocnet deep multitask learning for semantic visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "local descriptors optimized for average precision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")

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)