human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "topometric localization with deep learning"
  Abstract: Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods.

Connected through hyperedge HE-1 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "geometric consistency for self supervised end to end visual odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "improved visual relocalization by discovering anchor points"

Connected through hyperedge HE-2 (induced by source paper "self driving cars a survey"):
  with members:
    - "traffic light recognition using deep learning and prior maps for autonomous cars"
    - "effortless deep training for traffic sign detection using templates and arbitrary natural images"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "dct maps compact differentiable lidar maps based on the cosine transform"
    - "visual global localization with a hybrid wnn cnn approach"
    - "map memorization and forgetting in the iara autonomous car"
    - "mapping road lanes using laser remission and deep neural networks"
    - "roadtracer automatic extraction of road networks from aerial images"

Connected through hyperedge HE-3 (induced by source paper "gslam a general slam framework and benchmark"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ldso direct sparse odometry with loop closure"
    - "high quality monocular depth estimation via transfer learning"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "geonet unsupervised learning of dense depth optical flow and camera pose"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "optical flow in mostly rigid scenes"
    - "unsupervised learning of depth and ego motion from video"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "visual global localization with a hybrid wnn cnn approach"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "towards cnn map compression for camera relocalisation"
    - "relative camera pose estimation using convolutional neural networks"
    - "planet photo geolocation with convolutional neural networks"
    - "dsac differentiable ransac for camera localization"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning to see by moving"

Connected through hyperedge HE-6 (induced by source paper "camera relocalization by predicting pairwise relative poses using convolutional neural network"):
  with members:
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"
    - "generic 3d representation via pose estimation and matching"
    - "deep image homography estimation"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-7 (induced by source paper "dpc net deep pose correction for visual localization"):
  with members:
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "probe gk predictive robust estimation using generalized kernels"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "benchmarking deep reinforcement learning for continuous control"
    - "backprop kf learning discriminative deterministic state estimators"

Question: Please tell me which class the center node 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)