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: "elastic lidar fusion dense map centric continuous time slam"
  Abstract: The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these advantages, its offline property caused by the requirement of global batch optimization is critically hindering its relevance for real-time and life-long applications. In this paper, we present a dense map-centric SLAM method based on a continuous-time trajectory to cope with this problem. The proposed system locally functions in a similar fashion to conventional Continuous-Time SLAM (CT-SLAM). However, it removes the need for global trajectory optimization by introducing map deformation. The computational complexity of the proposed approach for loop closure does not depend on the operation time, but only on the size of the space it explored before the loop closure. It is therefore more suitable for long term operation compared to the conventional CT-SLAM. Furthermore, the proposed method reduces uncertainty in the reconstructed dense map by using probabilistic surface element (surfel) fusion. We demonstrate that the proposed method produces globally consistent maps without global batch trajectory optimization, and effectively reduces LiDAR noise by surfel fusion.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal camera lidar calibration a targetless and structureless approach"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "probabilistic surfel fusion for dense lidar mapping"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-2 (induced by source paper "r lins a robocentric lidar inertial state estimator for robust and efficient navigation"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "quaternion kinematics for the error state kalman filter"

Connected through hyperedge HE-3 (induced by source paper "in2laama inertial lidar localisation autocalibration and mapping"):
  with members:
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "imls slam scan to model matching based on 3d data"
    - "a simple observer for gyro and accelerometer biases in land navigation systems"
    - "an intuitive approach to inertial sensor bias estimation"

Connected through hyperedge HE-4 (induced by source paper "synthetic human model dataset for skeleton driven non rigid motion tracking and 3d reconstruction"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "skeleton driven non rigid motion tracking and 3d reconstruction"
    - "probabilistic surfel fusion for dense lidar mapping"

Connected through hyperedge HE-5 (induced by source paper "tightly coupled 3d lidar inertial odometry and mapping"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "complex urban lidar data set"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-6 (induced by source paper "robust photogeometric localization over time for map centric loop closure"):
  with members:
    - "local descriptor for robust place recognition using lidar intensity"

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)