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: "end to end learning of multi sensor 3d tracking by detection"
  Abstract: In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.

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 "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "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"
    - "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-3 (induced by source paper "who is driving around me unique vehicle instance classification using deep neural features"):
  with members:
    - "yolov3 an incremental improvement"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deepsignals predicting intent of drivers through visual signals"):
  with members:
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "mask r cnn"
    - "accurate single stage detector using recurrent rolling convolution"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "deep watershed transform for instance segmentation"
    - "pyramid scene parsing network"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-7 (induced by source paper "monocular plan view networks for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "conditional affordance learning for driving in urban environments"
    - "driving policy transfer via modularity and abstraction"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "deep object centric representations for generalizable robot learning"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-8 (induced by source paper "fantrack 3d multi object tracking with feature association network"):
  with members:
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "focal loss for dense object detection"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "online multi target tracking using recurrent neural 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)