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: "robust multi modality multi object tracking"
  Abstract: Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects. Most current approaches for multi-sensor multi-object tracking are either lack of reliability by tightly relying on a single input source (e.g., center camera), or not accurate enough by fusing the results from multiple sensors in post processing without fully exploiting the inherent information. In this study, we design a generic sensor-agnostic multi-modality MOT framework (mmMOT), where each modality (i.e., sensors) is capable of performing its role independently to preserve reliability, and further improving its accuracy through a novel multi-modality fusion module. Our mmMOT can be trained in an end-to-end manner, enables joint optimization for the base feature extractor of each modality and an adjacency estimator for cross modality. Our mmMOT also makes the first attempt to encode deep representation of point cloud in data association process in MOT. We conduct extensive experiments to evaluate the effectiveness of the proposed framework on the challenging KITTI benchmark and report state-of-the-art performance. Code and models are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi modality cascaded fusion technology for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "tracking without bells and whistles"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "class specific anchoring proposal for 3d object recognition in lidar and rgb images"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"

Connected through hyperedge HE-3 (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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "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-4 (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"
    - "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"

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)