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: "ls vo learning dense optical subspace for robust visual odometry estimation"
  Abstract: This work proposes a novel deep network architecture to solve the camera ego-motion estimation problem. A motion estimation network generally learns features similar to optical flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an autoencoder network to find a nonlinear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture latent space visual odometry (LS-VO). The experiments show that LS-VO achieves a considerable increase in performances with respect to baselines, while the number of parameters of the estimation network only slightly increases.

Connected through hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep 6 dof tracking"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of camera pose with compositional re estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "megadepth learning single view depth prediction from internet photos"
    - "ba net dense bundle adjustment network"

Connected through hyperedge HE-3 (induced by source paper "scene motion decomposition for learnable visual odometry"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deeptam deep tracking and mapping"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"

Connected through hyperedge HE-4 (induced by source paper "poseconvgru a monocular approach for visual ego motion estimation by learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "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"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "direct sparse odometry"
    - "convolutional gated recurrent networks for video segmentation"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-5 (induced by source paper "discoman dataset of indoor scenes for odometry mapping and navigation"):
  with members:
    - "scene motion decomposition for learnable visual odometry"
    - "panoptic segmentation"
    - "open3d a modern library for 3d data processing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "egocentric spatial memory"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "slanted stixels representing san francisco s steepest streets"

Connected through hyperedge HE-6 (induced by source paper "training deep slam on single frames"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "deeppco end to end point cloud odometry through deep parallel neural network"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "selective sensor fusion for neural visual inertial odometry"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-8 (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"
    - "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"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

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)