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: "sensor fusion for joint 3d object detection and semantic segmentation"
  Abstract: In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector. We propose a method for fusing image data with the LiDAR data and show that this sensor fusion method improves the detection performance of the model especially at long ranges. The addition of image data is straightforward and does not require image labels. Furthermore, we expand the capabilities of the model to perform 3D semantic segmentation in addition to 3D object detection. On a large benchmark dataset, we demonstrate our approach achieves state-of-the-art performance on both object detection and semantic segmentation while maintaining a low runtime.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "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 "laserflow efficient and probabilistic object detection and motion forecasting"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "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-3 (induced by source paper "multi modality cascaded fusion technology for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "tracking without bells and whistles"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "robust multi modality multi object tracking"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "class specific anchoring proposal for 3d object recognition in lidar and rgb images"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "lidar guided small obstacle segmentation"):
  with members:
    - "deep high resolution representation learning for visual recognition"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "rethinking atrous convolution for semantic image segmentation"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "lost and found detecting small road hazards for self driving vehicles"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"

Connected through hyperedge HE-5 (induced by source paper "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "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-6 (induced by source paper "pointpainting sequential fusion for 3d object detection"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "class balanced grouping and sampling for point cloud 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "latte accelerating lidar point cloud annotation via sensor fusion one click annotation and tracking"
    - "fast scnn fast semantic segmentation network"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-8 (induced by source paper "learning an uncertainty aware object detector for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"

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)