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: "probabilistic surfel fusion for dense lidar mapping"
  Abstract: With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. However, none of the previous approaches were able to effectively exploit this redundancy in a dense LiDAR mapping problem. In this paper, we present a new approach for dense LiDAR mapping using probabilistic surfel fusion. The proposed system is capable of reconstructing a high-quality dense surface element (surfel) map from spatially redundant multiple views. This is achieved by a proposed probabilistic surfel fusion along with a geometry considered data association. The proposed surfel data association method considers surface resolution as well as high measurement uncertainty along its beam direction which enables the mapping system to be able to control surface resolution without introducing spatial digitization. The proposed fusion method successfully suppresses the map noise level by considering measurement noise caused by laser beam incident angle and depth distance in a Bayesian filtering framework. Experimental results with simulated and real data for the dense surfel mapping prove the ability of the proposed method to accurately find the canonical form of the environment without further post-processing.

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"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "elastic lidar fusion dense map centric continuous time slam"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-2 (induced by source paper "tigris architecture and algorithms for 3d perception in point clouds"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "imls slam scan to model matching based on 3d data"
    - "visualization and labeling of point clouds in virtual reality"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-3 (induced by source paper "lic fusion lidar inertial camera odometry"):
  with members:
    - "robocentric visual inertial odometry"
    - "limo lidar monocular visual odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"

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"
    - "elastic lidar fusion dense map centric continuous time slam"

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)