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: "slam auto complete using an emergency map"
  Abstract: Often, robots have to work in situations where classic sensors do not perform well. Dust, fire or smoke can corrupt laser scanners and camera measurements. In those situation, it is important to be able to reason on non-visible and unknown part of the environment. One way to achieve this is to integrate prior information in the SLAM. While previous research mainly focused on using accurate maps or aerial images as prior in the SLAM it is not always possible to get those, especially indoor. #R##N#We developed a formulation of graph-based SLAM, incorporating information from a rough prior. We focus on emergency-maps. Emergency-maps are easy to get and a support extensively used, e.g., by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of all rooms are typically not consistent. #R##N#We create a graph associating information from both types of maps. The graph is optimized, using a combination of robust kernels, fusing information from the emergency and the SLAM-map into one map even when faced with scale inaccuracies and inexact start poses. #R##N#We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments on an office environment show that, we can handle up to 69% of wrong correspondences and still get the expected result. By incorporating the emergency-map's information in the SLAM-map, the robot can navigate and explore while taking into account places it has not yet seen. We also show that the emergency-map is enhanced by adding information not represented such as closed doors or new walls.

Connected through hyperedge HE-1 (induced by source paper "2d map alignment with region decomposition"):
  with members:
    - "scikit image image processing in python"
    - "the numpy array a structure for efficient numerical computation"
    - "point set registration coherent point drift"

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)