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: "keyframe based visual inertial online slam with relocalization"
  Abstract: Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our system supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public indoor benchmark datasets and on newly recorded outdoor sequences. We demonstrate state-of-the-art performance of our system compared to a visual-inertial odometry method and baseline visual SLAM approaches in recovering the trajectory of the camera.

Connected through hyperedge HE-1 (induced by source paper "large scale real time visual inertial localization revisited"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "identifying robust landmarks in feature based maps"
    - "hybrid scene compression for visual localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-2 (induced by source paper "vipr visual odometry aided pose regression for 6dof camera localization"):
  with members:
    - "distancenet estimating traveled distance from monocular images using a recurrent convolutional neural network"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "geometric consistency for self supervised end to end visual odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-3 (induced by source paper "visual inertial mapping with non linear factor recovery"):
  with members:
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "visual inertial monocular slam with map reuse"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-4 (induced by source paper "relocalization global optimization and map merging for monocular visual inertial slam"):
  with members:
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "exploring spatial context for 3d semantic segmentation of point clouds"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)