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: "2d visual place recognition for domestic service robots at night"
  Abstract: Domestic service robots such as lawn mowing and vacuum cleaning robots are the most numerous consumer robots in existence today. While early versions employed random exploration, recent systems fielded by most of the major manufacturers have utilized range-based and visual sensors and user-placed beacons to enable robots to map and localize. However, active range and visual sensing solutions have the disadvantages of being intrusive, expensive, or only providing a 1D scan of the environment, while the requirement for beacon placement imposes other practical limitations. In this paper we present a passive and potentially cheap vision-based solution to 2D localization at night that combines easily obtainable day-time maps with low resolution contrast-normalized image matching algorithms, image sequence-based matching in two-dimensions, place match interpolation and recent advances in conventional low light camera technology. In a range of experiments over a domestic lawn and in a lounge room, we demonstrate that the proposed approach enables 2D localization at night, and analyse the effect on performance of varying odometry noise levels, place match interpolation and sequence matching length. Finally we benchmark the new low light camera technology and show how it can enable robust place recognition even in an environment lit only by a moonless sky, raising the tantalizing possibility of being able to apply all conventional vision algorithms, even in the darkest of nights.

Connected through hyperedge HE-1 (induced by source paper "robust place categorization with deep domain generalization"):
  with members:
    - "autodial automatic domain alignment layers"
    - "learning deep nbnn representations for robust place categorization"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "deep learning features at scale for visual place recognition"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "imagenet pre trained models with batch normalization"
    - "revisiting batch normalization for practical domain adaptation"

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)