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: "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
  Abstract: Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.

Connected through hyperedge HE-1 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "tracking without bells and whistles"
    - "track to reconstruct and reconstruct to track"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fantrack 3d multi object tracking with feature association network"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "pedestrian models for autonomous driving part i low level models from sensing to tracking"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "multi camera multi object tracking"
    - "posetrack joint multi person pose estimation and tracking"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "forecasting interactive dynamics of pedestrians with fictitious play"

Connected through hyperedge HE-3 (induced by source paper "who is driving around me unique vehicle instance classification using deep neural features"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "yolov3 an incremental improvement"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "you only look once unified real time object detection"
    - "near online multi target tracking with aggregated local flow descriptor"
    - "microsoft coco common objects in context"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-4 (induced by source paper "a baseline for 3d multi object tracking"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "pointit a fast tracking framework based on 3d instance segmentation"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "simple online and realtime tracking with a deep association metric"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "track to reconstruct and reconstruct to track"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mots multi object tracking and segmentation"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "robust dense mapping for large scale dynamic environments"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "learning optimal parameters for multi target tracking with contextual interactions"

Connected through hyperedge HE-8 (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"
    - "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"

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)