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 "a generative map for image based camera localization"):
  Abstract: In image-based camera localization systems, information about the environment is usually stored in some representation, which can be referred to as a map. Conventionally, most maps are built upon hand-crafted features. Recently, neural networks have attracted attention as a data-driven map representation, and have shown promising results in visual localization. However, these neural network maps are generally hard to interpret by human. A readable map is not only accessible to humans, but also provides a way to be verified when the ground truth pose is unavailable. To tackle this problem, we propose Generative Map, a new framework for learning human-readable neural network maps, by combining a generative model with the Kalman filter, which also allows it to incorporate additional sensor information such as stereo visual odometry. For evaluation, we use real world images from the 7-Scenes and Oxford RobotCar datasets. We demonstrate that our Generative Map can be queried with a pose of interest from the test sequence to predict an image, which closely resembles the true scene. For localization, we show that Generative Map achieves comparable performance with current regression models. Moreover, our framework is trained completely from scratch, unlike regression models which rely on large ImageNet pretrained networks.

Includes members:
  Member "variational autoencoded regression high dimensional regression of visual data on complex manifold":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural approximation of an auto regressive process through confidence guided sampling")
  Member "learning models for visual 3d localization with implicit mapping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geometry aware neural rendering")
  Member "vidloc a deep spatio temporal model for 6 dof video clip relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "deep variational canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning robust representations via multi view information bottleneck")
  Member "image based localization using hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "geometry aware learning of maps for camera localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "generative models of visually grounded imagination":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multimodal generative models for compositional representation learning")

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)