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: "map management for efficient long term visual localization in outdoor environments"
  Abstract: We present a complete map management process for a visual localization system designed for multi-vehicle long- term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is non- trivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping backend. We address this problem with a two-fold map update paradigm capable of, either, adding new visual cues to the map, or updating co-observation statistics. The former, in combination with offline map summarization techniques, allows enhancing the appearance coverage of the lifelong map while keeping the map size limited. On the other hand, the latter is able to significantly boost the appearance-based landmark selection for efficient online localization without incurring any additional computational or storage burden. Our evaluation in challenging outdoor conditions shows that our proposed map management process allows building and maintaining maps for precise visual localization over long time spans in a tractable and scalable fashion.

Connected through hyperedge HE-1 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "multi attention recurrent network for human communication comprehension"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "real time environmentally robust 3d lidar localization"):
  with members:
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "key ingredients of self driving cars"
    - "appearance based landmark selection for efficient long term visual localization"
    - "laser map aided visual inertial localization in changing environment"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "scalable change retrieval using deep 3d neural codes"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "detection by localization maintenance free change object detector"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "lightweight unsupervised deep loop closure"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "learning deep structured network for weakly supervised change detection"
    - "yolo9000 better faster stronger"

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)