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: "boosting lidar based semantic labeling by cross modal training data generation"
  Abstract: Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for efficient inference. Further, we propose an automated process for large-scale cross-modal training data generation called Autolabeling, in order to boost semantic labeling performance while keeping the manual annotation effort low. The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset. LiLaNet is shown to significantly outperform current state-of-the-art CNN architectures for LiDAR data. Applying our automatically generated large-scale training data yields a boost of up to 14 percentage points compared to networks trained on manually annotated data only.

Connected through hyperedge HE-1 (induced by source paper "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"):
  with members:
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "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"
    - "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-2 (induced by source paper "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "semantic foggy scene understanding with synthetic data"
    - "optimal sensor data fusion architecture for object detection in adverse weather conditions"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "deep semantic classification for 3d lidar data"
    - "fast lidar based road detection using fully convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "analyzing the cross sensor portability of neural network architectures for lidar based semantic labeling"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "self supervised generalisation with meta auxiliary learning"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "birdnet a 3d object detection framework from lidar information"
    - "semantic segmentation of 3d lidar data in dynamic scene using semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "latte accelerating lidar point cloud annotation via sensor fusion one click annotation and tracking"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "annotating object instances with a polygon rnn"

Connected through hyperedge HE-5 (induced by source paper "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"):
  with members:
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "domain adaptation for visual applications a comprehensive survey"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "semantic segmentation of 3d lidar data in dynamic scene using semi supervised learning"):
  with members:
    - "deep projective 3d semantic segmentation"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "segcloud semantic segmentation of 3d point clouds"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "octnet learning deep 3d representations at high resolutions"

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)