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: "a method to segment maps from different modalities using free space layout maoris map of ripples segmentation"
  Abstract: How to divide floor plans or navigation maps into a semantic representation, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most algorithms focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We have developed a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types. #R##N#Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple like patterns where pixel values vary quickly, and merging neighboring regions with similar values. #R##N#We identify a flaw in segmentation evaluation metric used in recent works and propose a more consistent metric. We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better Matthews correlation coefficient (MCC) than state of the art with 0.98 compared to 0.85 for a recent Voronoi-based segmentation method and 0.78 for the DuDe segmentation method. We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains a MCC of 0.82 against 0.40 for the Voronoi-based segmentation method and 0.45 for DuDe.

Connected through hyperedge HE-1 (induced by source paper "mining minimal map segments for visual place classifiers"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "soft nms improving object detection with one line of code"
    - "track then decide category agnostic vision based multi object tracking"
    - "x view graph based semantic multi view localization"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "houseexpo a large scale 2d indoor layout dataset for learning based algorithms on mobile robots"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "an analytical lidar sensor model based on ray path information"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "proximal policy optimization algorithms"
    - "semantic scene completion from a single depth image"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

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)