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: "heterogeneous multi sensor calibration based on graph optimization"
  Abstract: Many robotics and mapping systems contain multiple sensors to perceive the environment. Extrinsic parameter calibration, the identification of the position and rotation transform between the frames of the different sensors, is critical to fuse data from different sensors. When obtaining multiple camera to camera, lidar to camera and lidar to lidar calibration results, inconsistencies are likely. We propose a graph-based method to refine the relative poses of the different sensors. We demonstrate our approach using our mapping robot platform, which features twelve sensors that are to be calibrated. The experimental results confirm that the proposed algorithm yields great performance.

Connected through hyperedge HE-1 (induced by source paper "fast 2d map matching based on area graphs"):
  with members:
    - "towards generation and evaluation of comprehensive mapping robot datasets"
    - "furniture free mapping using 3d lidars"
    - "area graph generation of topological maps using the voronoi diagram"
    - "2d map alignment with region decomposition"

Connected through hyperedge HE-2 (induced by source paper "furniture free mapping using 3d lidars"):
  with members:
    - "fast 2d map matching based on area graphs"
    - "towards generation and evaluation of comprehensive mapping robot datasets"
    - "mapping with reflection detection and utilization of reflection in 3d lidar scans"
    - "area graph generation of topological maps using the voronoi diagram"

Connected through hyperedge HE-3 (induced by source paper "furniture free mapping"):
  with members:
    - "fast 2d map matching based on area graphs"
    - "towards generation and evaluation of comprehensive mapping robot datasets"
    - "mapping with reflection detection and utilization of reflection in 3d lidar scans"
    - "area graph generation of topological maps using the voronoi diagram"

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)