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: "area graph generation of topological maps using the voronoi diagram"
  Abstract: Representing a scanned map of the real environment as a topological structure is an important research topic in robotics. Since topological representations of maps save a huge amount of map storage space and online computing time, they are widely used in fields such as path planning, map matching, and semantic mapping. We use a topological map representation, the Area Graph, in which the vertices represent areas and edges represent passages. The Area Graph is developed from a pruned Voronoi Graph, the Topology Graph. We also employ a simple room detection algorithm to compensate the fact that the Voronoi Graph gets unstable in open areas. We claim that our area segmentation method is superior to state-of-the-art approaches in complex indoor environments and support this claim with a number of experiments.

Connected through hyperedge HE-1 (induced by source paper "fast 2d map matching based on area graphs"):
  with members:
    - "heterogeneous multi sensor calibration based on graph optimization"
    - "towards generation and evaluation of comprehensive mapping robot datasets"
    - "furniture free mapping using 3d lidars"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "furniture free mapping"):
  with members:
    - "fast 2d map matching based on area graphs"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "room detection for topological maps"):
  with members:
    - "fast 2d map matching based on area graphs"

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)