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: "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
  Abstract: Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images. However, in most cases image-sequences, rather only single images, are readily available. To this extent, none of the proposed learning-based approaches exploit the valuable constraint of temporal smoothness, often leading to situations where the per-frame error is larger than the camera motion. In this paper we propose a recurrent model for performing 6-DoF localization of video-clips. We find that, even by considering only short sequences (20 frames), the pose estimates are smoothed and the localization error can be drastically reduced. Finally, we consider means of obtaining probabilistic pose estimates from our model. We evaluate our method on openly-available real-world autonomous driving and indoor localization datasets.

Connected through hyperedge HE-1 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "atloc attention guided camera localization"
    - "deep hough voting for 3d object detection in point clouds"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "flownet3d learning scene flow in 3d point clouds"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "to learn or not to learn visual localization from essential matrices"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-5 (induced by source paper "introduction to camera pose estimation with deep learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "learning loss for active learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "deep auxiliary learning for visual localization and odometry"

Connected through hyperedge HE-6 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"

Connected through hyperedge HE-7 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression"):
  with members:
    - "lf net learning local features from images"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "improved visual relocalization by discovering anchor points"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"

Connected through hyperedge HE-8 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "adversarial joint image and pose distribution learning for camera pose regression and refinement"
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

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)