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: "deepvo a deep learning approach for monocular visual odometry"
  Abstract: Deep Learning based techniques have been adopted with precision to solve a lot of standard computer vision problems, some of which are image classification, object detection and segmentation. Despite the widespread success of these approaches, they have not yet been exploited largely for solving the standard perception related problems encountered in autonomous navigation such as Visual Odometry (VO), Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem of Monocular Visual Odometry using a Deep Learning-based framework, instead of the regular 'feature detection and tracking' pipeline approaches. Several experiments were performed to understand the influence of a known/unknown environment, a conventional trackable feature and pre-trained activations tuned for object classification on the network's ability to accurately estimate the motion trajectory of the camera (or the vehicle). Based on these observations, we propose a Convolutional Neural Network architecture, best suited for estimating the object's pose under known environment conditions, and displays promising results when it comes to inferring the actual scale using just a single camera in real-time.

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 "poseconvgru a monocular approach for visual ego motion estimation by learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "direct sparse odometry"
    - "convolutional gated recurrent networks for video segmentation"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-3 (induced by source paper "multivariate uncertainty in deep learning"):
  with members:
    - "real time object pose estimation with pose interpreter networks"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"
    - "backprop kf learning discriminative deterministic state estimators"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "ground plane based absolute scale estimation for monocular visual odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "probabilistic global scale estimation for monoslam based on generic object detection"
    - "a survey of structure from motion"
    - "inertial based scale estimation for structure from motion on mobile devices"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "vlocnet deep multitask learning for semantic visual localization and odometry"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "image based localization using hourglass networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "deep global relative networks for end to end 6 dof visual localization and odometry"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "geometric consistency for self supervised end to end visual odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "sfm net learning of structure and motion from video"
    - "a survey of structure from motion"

Connected through hyperedge HE-7 (induced by source paper "deep auxiliary learning for visual localization and odometry"):
  with members:
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "image based localization using hourglass networks"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "relative camera pose estimation using convolutional neural networks"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-8 (induced by source paper "deep visual odometry methods for mobile robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "direct monocular odometry using points and lines"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"

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)