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: "fast 2d map matching based on area graphs"
  Abstract: We present a novel area matching algorithm for merging two different 2D grid maps. There are many approaches to address this problem, nevertheless, most previous work is built on some assumptions, such as rigid transformation, or similar scale and modalities of two maps. In this work we propose a 2D map matching algorithm based on area segmentation. We transfer general 2D occupancy grid maps to an area graph representation, then compute the correct results by voting in that space. In the experiments, we compare with a state-of-the-art method applied to the matching of sensor maps with ground truth layout maps. The experiment shows that our algorithm has a better performance on large-scale maps and a faster computation speed.

Connected through hyperedge HE-1 (induced by source paper "furniture free mapping using 3d lidars"):
  with members:
    - "heterogeneous multi sensor calibration based on graph optimization"
    - "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-2 (induced by source paper "furniture free mapping"):
  with members:
    - "heterogeneous multi sensor calibration based on graph optimization"
    - "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 "room detection for topological maps"):
  with members:
    - "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)