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: "deconvolutional networks for point cloud vehicle detection and tracking in driving scenarios"
  Abstract: Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtaining breakthrough results in these perceptive tasks. However, DL research has not yet advanced much towards processing 3D point clouds from lidar range-finders. These sensors are very common in autonomous vehicles since, despite not providing as semantically rich information as images, their performance is more robust under harsh weather conditions than vision sensors. In this paper we present a full vehicle detection and tracking system that works with 3D lidar information only. Our detection step uses a Convolutional Neural Network (CNN) that receives as input a featured representation of the 3D information provided by a Velodyne HDL-64 sensor and returns a per-point classification of whether it belongs to a vehicle or not. The classified point cloud is then geometrically processed to generate observations for a multi-object tracking system implemented via a number of Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position and velocity of the surrounding vehicles. The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach. Our lidar-based approach uses about a 4% of the data needed for an image-based detector with similarly competitive results.

Connected through hyperedge HE-1 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hallucinating dense optical flow from sparse lidar for autonomous vehicles"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"

Connected through hyperedge HE-3 (induced by source paper "improving map re localization with deep movable objects segmentation on 3d lidar point clouds"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "birdnet a 3d object detection framework from lidar information"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "cmrnet camera to lidar map registration"):
  with members:
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "geometry aware learning of maps for camera localization"
    - "regnet multimodal sensor registration using deep neural networks"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-5 (induced by source paper "deep generative modeling of lidar data"):
  with members:
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "the relativistic discriminator a key element missing from standard gan"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dscnet replicating lidar point clouds with deep sensor cloning"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep semantic classification for 3d lidar data"

Connected through hyperedge HE-6 (induced by source paper "hallucinating dense optical flow from sparse lidar for autonomous vehicles"):
  with members:
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "joint coarse and fine reasoning for deep optical flow"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-7 (induced by source paper "deep lidar cnn to understand the dynamics of moving vehicles"):
  with members:
    - "progressive neural networks"
    - "learning motion patterns in videos"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "multi view 3d object detection network for autonomous driving"
    - "deep residual learning for image recognition"
    - "learning to see by moving"

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)