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: "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
  Abstract: This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow. Given this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative pose and furthermore reconstruct the vehicle trajectory. In order to learn the correlation between motion directions, the Bivariate Gaussian modelling is employed in the loss function. The L-VO network achieves an overall performance of 2.68% for average translational error and 0.0143 deg/m for average rotational error on the KITTI odometry benchmark. Moreover, the learned depth is fully leveraged to generate a dense 3D map. As a result, an entire visual SLAM system, that is, learning monocular odometry combined with dense 3D mapping, is achieved.

Connected through hyperedge HE-1 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-2 (induced by source paper "sterefo efficient image refocusing with stereo vision"):
  with members:
    - "real time self adaptive deep stereo"
    - "synthetic depth of field with a single camera mobile phone"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deeplens shallow depth of field from a single image"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-3 (induced by source paper "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "pyramid stereo matching network"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-4 (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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "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-5 (induced by source paper "distancenet estimating traveled distance from monocular images using a recurrent convolutional neural network"):
  with members:
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "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"
    - "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-6 (induced by source paper "online deep reinforcement learning for autonomous uav navigation and exploration of outdoor environments"):
  with members:
    - "visual appearance analysis of forest scenes for monocular slam"
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"
    - "transfer and online reinforcement learning in stt mram based embedded systems for autonomous drones"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "rainbow combining improvements in deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning what data to learn"

Connected through hyperedge HE-7 (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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "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.RO(Robotics)