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: "place categorization and semantic mapping on a mobile robot"
  Abstract: In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module.

Connected through hyperedge HE-1 (induced by source paper "learning topometric semantic maps from occupancy grids"):
  with members:
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "topological mapping for manhattan like repetitive environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "gibson env real world perception for embodied agents"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deduce diverse scene detection methods in unseen challenging environments"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "robust place categorization with deep domain generalization"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-4 (induced by source paper "a realtime autonomous robot navigation framework for human like high level interaction and task planning in global dynamic environment"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "xception deep learning with depthwise separable convolutions"
    - "learning deconvolution network for semantic segmentation"
    - "orb slam a versatile and accurate monocular slam system"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-5 (induced by source paper "incremental semantic mapping with unsupervised on line learning"):
  with members:
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"

Connected through hyperedge HE-7 (induced by source paper "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "focal loss for dense object detection"
    - "icnet for real time semantic segmentation on high resolution images"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "real time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "uav visual teach and repeat using only semantic object features"):
  with members:
    - "slam with objects using a nonparametric pose graph"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "faster r cnn towards real time object detection with region proposal networks"

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)