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: "beyond pixels leveraging geometry and shape cues for online multi object tracking"
  Abstract: This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose, shape, and motion. The proposed costs are agnostic to the data association method and can be incorporated into any optimization framework to output the pairwise data associations. These costs are easy to implement, can be computed in real-time, and complement each other to account for possible errors in a tracking-by-detection framework. We perform an extensive analysis of the designed costs and empirically demonstrate consistent improvement over the state-of-the-art under varying conditions that employ a range of object detectors, exhibit a variety in camera and object motions, and, more importantly, are not reliant on the choice of the association framework. We also show that, by using the simplest of associations frameworks (two-frame Hungarian assignment), we surpass the state-of-the-art in multi-object-tracking on road scenes. More qualitative and quantitative results can be found at the following URL: this https URL

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"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "improved semantic stixels via multimodal sensor fusion"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"

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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "how transferable are the datasets collected by active learners"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"

Connected through hyperedge HE-5 (induced by source paper "leveraging shape completion for 3d siamese tracking"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "learning depth with convolutional spatial propagation network"
    - "real time mdnet"
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "segstereo exploiting semantic information for disparity estimation"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-6 (induced by source paper "learning multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "seamless scene segmentation"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-7 (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-8 (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"
    - "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"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"

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.RO(Robotics)