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: "deep global relative networks for end to end 6 dof visual localization and odometry"
  Abstract: Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global networks based on Recurrent Convolutional Neural Networks (RCNNs) to improve the monocular localization accuracy. Indeed, the relative sub-networks are implemented to smooth the VO trajectory, while global subnetworks are designed to avoid drift problem. All the parameters are jointly optimized using Cross Transformation Constraints (CTC), which represents temporal geometric consistency of the consecutive frames, and Mean Square Error (MSE) between the predicted pose and ground truth. The experimental results on both indoor and outdoor datasets show that our method outperforms other state-of-the-art learning-based VO methods in terms of pose accuracy.

Connected through hyperedge HE-1 (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 auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-2 (induced by source paper "distancenet estimating traveled distance from monocular images using a recurrent convolutional neural network"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "magicvo end to end monocular visual odometry through deep bi directional recurrent convolutional neural network"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "estimating 3d camera pose from 2d pedestrian trajectories"):
  with members:
    - "introduction to camera pose estimation with deep learning"
    - "sports camera calibration via synthetic data"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "adaptive leader follower formation control and obstacle avoidance via deep reinforcement learning"):
  with members:
    - "an introduction to deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "learning montezuma s revenge from a single demonstration"
    - "learning to navigate in cities without a map"
    - "learning deployable navigation policies at kilometer scale from a single traversal"
    - "one shot reinforcement learning for robot navigation with interactive replay"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "deep learning based wearable assistive system for visually impaired people"):
  with members:
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "wearable travel aid for environment perception and navigation of visually impaired people"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "learning transferable uav for forest visual perception"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "the cityscapes dataset for semantic urban scene understanding"

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)