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: "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
  Abstract: Online multi-object tracking (MOT) is extremely important for high-level spatial reasoning and path planning for autonomous and highly-automated vehicles. In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks. This work is inspired by traditional tracking-by-detection approaches in computer vision, with some key differences - First, we track objects across multiple cameras and across different sensor modalities. This is done by fusing object proposals across sensors accurately and efficiently. Second, the objects of interest (targets) are tracked directly in the real world. This is a departure from traditional techniques where objects are simply tracked in the image plane. Doing so allows the tracks to be readily used by an autonomous agent for navigation and related tasks. #R##N#To verify the effectiveness of our approach, we test it on real world highway data collected from a heavily sensorized testbed capable of capturing full-surround information. We demonstrate that our framework is well-suited to track objects through entire maneuvers around the ego-vehicle, some of which take more than a few minutes to complete. We also leverage the modularity of our approach by comparing the effects of including/excluding different sensors, changing the total number of sensors, and the quality of object proposals on the final tracking result.

Connected through hyperedge HE-1 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "robust multi modality multi object tracking"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "online visual robot tracking and identification using deep lstm networks"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-2 (induced by source paper "semantic sensor fusion from camera to sparse lidar information"):
  with members:
    - "automated evaluation of semantic segmentation robustness for autonomous driving"
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-3 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-4 (induced by source paper "robust multi modality multi object tracking"):
  with members:
    - "deep multi sensor lane detection"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "combined image and world space tracking in traffic scenes"
    - "the perfect match 3d point cloud matching with smoothed densities"

Connected through hyperedge HE-5 (induced by source paper "vehicular multi object tracking with persistent detector failures"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "a baseline for 3d multi object tracking"
    - "robust multi modality multi object tracking"
    - "towards multi object detection and tracking in urban scenario under uncertainties"
    - "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-6 (induced by source paper "roadtrack realtime tracking of road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "a hybrid data association framework for robust online multi object tracking"

Connected through hyperedge HE-7 (induced by source paper "roadtrack tracking road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "a hybrid data association framework for robust online multi object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-8 (induced by source paper "3d bat a semi automatic web based 3d annotation toolbox for full surround multi modal data streams"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "ground plane polling for 6dof pose estimation of objects on the road"
    - "the impact of automatic pre annotation in clinical note data element extraction the clean tool"
    - "semantic instance annotation of street scenes by 3d to 2d label transfer"

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)