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: "capturing object detection uncertainty in multi layer grid maps"
  Abstract: We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets.

Connected through hyperedge HE-1 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "disentangling monocular 3d object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "fcos fully convolutional one stage object detection"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "objects as points"

Connected through hyperedge HE-2 (induced by source paper "single stage object detection from top view grid maps on custom sensor setups"):
  with members:
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pointpillars fast encoders for object detection from point clouds"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "birdnet a 3d object detection framework from lidar information"
    - "strong weak distribution alignment for adaptive object detection"
    - "complex yolo real time 3d object detection on point clouds"

Connected through hyperedge HE-3 (induced by source paper "monocular 3d object detection with pseudo lidar point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "ipod intensive point based object detector for point cloud"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-4 (induced by source paper "monoloco monocular 3d pedestrian localization and uncertainty estimation"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pifpaf composite fields for human pose estimation"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"

Connected through hyperedge HE-5 (induced by source paper "can we trust you on calibration of a probabilistic object detector for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "evaluating merging strategies for sampling based uncertainty techniques in object detection"

Connected through hyperedge HE-6 (induced by source paper "uncertainty estimation in one stage object detection"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "yolov3 an incremental improvement"
    - "calibrating uncertainties in object localization task"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "dropout sampling for robust object detection in open set conditions"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

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)