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: "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
  Abstract: In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view mapping. At the core, it utilizes a variational encoder–decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a two-dimensional top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic mapping approach with flat-plane assumption by more than 12% mean intersection-over-union. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256 × 512 pixels and an output map with 64 × 64 occupancy grid cells using a Titan V GPU.

Connected through hyperedge HE-1 (induced by source paper "monolayout amodal scene layout from a single image"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "wise ale wide sample estimator for approximate latent embedding"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "probabilistic object detection definition and evaluation"

Connected through hyperedge HE-2 (induced by source paper "semantic foreground inpainting from weak supervision"):
  with members:
    - "privacy protection in street view panoramas using depth and multi view imagery"
    - "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "free form image inpainting with gated convolution"
    - "patch based image inpainting with generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "generative face completion"

Connected through hyperedge HE-3 (induced by source paper "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"):
  with members:
    - "learning to look around objects for top view representations of outdoor scenes"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "patch based image inpainting with generative adversarial networks"
    - "generative face completion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-4 (induced by source paper "road scene understanding by occupancy grid learning from sparse radar clusters using semantic segmentation"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "probably unknown deep inverse sensor modelling in radar"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "self supervised occupancy grid learning from sparse radar for autonomous driving"):
  with members:
    - "probably unknown deep inverse sensor modelling in radar"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"

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)