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: "lasernet an efficient probabilistic 3d object detector for autonomous driving"
  Abstract: In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the challenges of training on the range view.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-2 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving"):
  with members:
    - "deep evidential regression"
    - "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"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"

Connected through hyperedge HE-3 (induced by source paper "3d object detection from lidar data using distance depended feature extraction"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "pixor real time 3d object detection from point clouds"
    - "range adaptation for 3d object detection in lidar"
    - "a baseline for 3d multi object tracking"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "laserflow efficient and probabilistic object detection and motion forecasting"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-5 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles"):
  with members:
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "robust multi modality multi object tracking"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "online visual robot tracking and identification using deep lstm networks"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-6 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "disentangling monocular 3d object detection"
    - "a baseline for 3d multi object tracking"
    - "physics based rendering for improving robustness to rain"
    - "the apolloscape open dataset for autonomous driving and its application"

Connected through hyperedge HE-7 (induced by source paper "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "dense depth posterior ddp from single image and sparse range"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "a general pipeline for 3d detection of vehicles"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-8 (induced by source paper "starnet targeted computation for object detection in point clouds"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "end to end multi view fusion for 3d object detection in lidar point clouds"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "ipod intensive point based object detector for point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

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.CV(Computer Vision and Pattern Recognition)