human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fantrack 3d multi object tracking with feature association network"):
  Abstract: We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at this https URL.

Includes members:
  Member "precise synthetic image and lidar presil dataset for autonomous vehicle perception":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "beyond pixels leveraging geometry and shape cues for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset")
  Member "end to end learning of multi sensor 3d tracking by detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "mono camera 3d multi object tracking using deep learning detections and pmbm filtering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset")
  Member "joint 3d proposal generation and object detection from view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "arma nets expanding receptive field for dense prediction")

Question: Please tell me which class the source paper 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)