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: "complexer yolo real time 3d object detection and tracking on semantic point clouds"
  Abstract: Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics. In this work we present a novel fusion of neural network based state-of-the-art 3D detector and visual semantic segmentation in the context of autonomous driving. Additionally, we introduce Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable evaluation metric for comparison of object detections, which speeds up our inference time up to 20\% and halves training time. On top, we apply state-of-the-art online multi target feature tracking on the object measurements to further increase accuracy and robustness utilizing temporal information. Our experiments on KITTI show that we achieve same results as state-of-the-art in all related categories, while maintaining the performance and accuracy trade-off and still run in real-time. Furthermore, our model is the first one that fuses visual semantic with 3D object detection.

Connected through hyperedge HE-1 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-2 (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"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "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-3 (induced by source paper "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "pointit a fast tracking framework based on 3d instance segmentation"
    - "autotrack a lightweight object detection and tracking system for the sae autodrive challenge"
    - "fantrack 3d multi object tracking with feature association network"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "flownet3d learning scene flow in 3d point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "focal loss in 3d object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"

Connected through hyperedge HE-4 (induced by source paper "stickypillars robust feature matching on point clouds using graph neural networks"):
  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"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "lo net deep real time lidar odometry"
    - "superglue learning feature matching with graph neural networks"
    - "loam_livox a fast robust high precision lidar odometry and mapping package for lidars of small fov"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"

Connected through hyperedge HE-5 (induced by source paper "birdnet end to end 3d object detection in lidar bird s eye view"):
  with members:
    - "pointpillars fast encoders for object detection from point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "feature pyramid networks for object detection"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "a baseline for 3d multi object tracking"):
  with members:
    - "how to train your deep multi object tracker"
    - "self supervised adaptation of high fidelity face models for monocular performance tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deepmot a differentiable framework for training multiple object trackers"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "fantrack 3d multi object tracking with feature association network"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "mots multi object tracking and segmentation"

Connected through hyperedge HE-7 (induced by source paper "vehicular multi object tracking with persistent detector failures"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "robust multi modality multi object tracking"
    - "towards multi object detection and tracking in urban scenario under uncertainties"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "pointpillars fast encoders for object detection from point clouds"
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "measurement wise occlusion in multi object tracking"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"

Connected through hyperedge HE-8 (induced by source paper "deep scnn based real time object detection for self driving vehicles using lidar temporal data"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "multi view 3d object detection network for autonomous driving"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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)