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: "an analytical lidar sensor model based on ray path information"
  Abstract: Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a model of the specific sensor. Over the past years, lidar sensors have become a popular choice for mapping and localization. However, many common lidar models perform poorly in unstructured, unpredictable environments, they lack a consistent physical model for both mapping and localization, and they do not exploit all the information the sensor provides, e.g. out-of-range measurements. In this letter, we introduce a consistent physical model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our model outperforms state-of-the-art sensor models in extensive real-world experiments.

Connected through hyperedge HE-1 (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"
    - "a method to segment maps from different modalities using free space layout maoris map of ripples segmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "evidential occupancy grid map augmentation using deep learning"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"
    - "occupancy grids a stochastic spatial representation for active robot perception"

Connected through hyperedge HE-3 (induced by source paper "dct maps compact differentiable lidar maps based on the cosine transform"):
  with members:
    - "closed form full map posteriors for robot localization with lidar sensors"

Connected through hyperedge HE-4 (induced by source paper "closed form full map posteriors for robot localization with lidar sensors"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian 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)