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: "the tum vi benchmark for evaluating visual inertial odometry"
  Abstract: Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024×1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.

Connected through hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "direct sparse odometry with rolling shutter"
    - "futuremapping the computational structure of spatial ai systems"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-2 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  with members:
    - "visual inertial odometry of aerial robots"
    - "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods"
    - "rins w robust inertial navigation system on wheels"
    - "ai imu dead reckoning"
    - "selfvio self supervised deep monocular visual inertial odometry and depth estimation"
    - "a mathematical framework for imu error propagation with applications to preintegration"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-3 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "lstm a search space odyssey"
    - "ridi robust imu double integration"

Connected through hyperedge HE-4 (induced by source paper "the newer college dataset handheld lidar inertial and vision with ground truth"):
  with members:
    - "online lidar slam for legged robots with robust registration and deep learned loop closure"
    - "robust legged robot state estimation using factor graph optimization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "unsupervised depth completion from visual inertial odometry"):
  with members:
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"

Connected through hyperedge HE-6 (induced by source paper "selective sensor fusion for neural visual inertial odometry"):
  with members:
    - "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"
    - "global pose estimation with an attention based recurrent network"
    - "modeling varying camera imu time offset in optimization based visual inertial odometry"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

Connected through hyperedge HE-7 (induced by source paper "gslam a general slam framework and benchmark"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ldso direct sparse odometry with loop closure"
    - "high quality monocular depth estimation via transfer learning"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topometric localization with deep learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-8 (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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "egocentric spatial memory"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "slanted stixels representing san francisco s steepest streets"

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)