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: "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
  Abstract: In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point- wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per frame), highly desirable for autonomous driving applications. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code and synthesized data will be open-sourced.

Connected through hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "instance aware representation learning and association for online multi person tracking"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "towards multi object detection and tracking in urban scenario under uncertainties"

Connected through hyperedge HE-3 (induced by source paper "3d mininet learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation"):
  with members:
    - "sloam semantic lidar odometry and mapping for forest inventory"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "lu net an efficient network for 3d lidar point cloud semantic segmentation based on end to end learned 3d features and u net"
    - "randla net efficient semantic segmentation of large scale point clouds"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-5 (induced by source paper "segmap segment based mapping and localization using data driven descriptors"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "learning a local feature descriptor for 3d lidar scans"
    - "seeing the wood for the trees reliable localization in urban and natural environments"
    - "in defense of the triplet loss for person re identification"
    - "semantic visual localization"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "segcloud semantic segmentation of 3d point clouds"
    - "compressed voxel based mapping using unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "deep learning for 3d point clouds a survey"
    - "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"
    - "fast scnn fast semantic segmentation network"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "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"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-7 (induced by source paper "squeezewave extremely lightweight vocoders for on device speech synthesis"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "waveglow a flow based generative network for speech synthesis"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "efficient neural audio synthesis"
    - "squeezenext hardware aware neural network design"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "soic semantic online initialization and calibration for lidar and camera"):
  with members:
    - "spatiotemporal camera lidar calibration a targetless and structureless approach"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"

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)