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: "evidential occupancy grid map augmentation using deep learning"
  Abstract: A detailed environment representation is a crucial component of automated vehicles. Using single range sensor scans, data is often too sparse and subject to occlusions. Therefore, we present a method to augment occupancy grid maps from single views to be similar to evidential occupancy maps acquired from different views using Deep Learning. To accomplish this, we estimate motion between subsequent range sensor measurements and create an evidential 3D voxel map in an extensive post-processing step. Within this voxel map, we explicitly model uncertainty using evidence theory and create a 2D projection using combination rules. As input for our neural networks, we use a multi-layer grid map consisting of the three features detections, transmissions and intensity, each for ground and non-ground measurements. Finally, we perform a quantitative and qualitative evaluation which shows that different network architectures accurately infer evidential measures in real-time.

Connected through hyperedge HE-1 (induced by source paper "learned enrichment of top view grid maps improves object detection"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pointpillars fast encoders for object detection from point clouds"
    - "attention u net learning where to look for the pancreas"
    - "occupancy networks learning 3d reconstruction in function space"
    - "focal loss for dense object detection"
    - "free space estimation using occupancy grids and dynamic object detection"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"

Connected through hyperedge HE-2 (induced by source paper "self supervised flow estimation using geometric regularization with applications to camera image and grid map sequences"):
  with members:
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"
    - "unsupervised learning of depth and ego motion from video"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "momo monocular motion estimation on manifolds"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "direct sparse odometry"

Connected through hyperedge HE-3 (induced by source paper "localization in aerial imagery with grid maps using locgan"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "object detection and classification in occupancy grid maps using deep convolutional networks"):
  with members:
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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)