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 "online multi object tracking framework with the gmphd filter and occlusion group management"):
  Abstract: In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: detection-to-track and track-to-track associations, which can recover the lost tracks and their switched IDs. In addition, the proposed framework is equipped with an object grouping management scheme which handles occlusion problems with two main parts. The first part is "track merging" which can merge the false positive tracks caused by false positive detections from occlusions, where the false positive tracks are usually occluded with a measure. The measure is the occlusion ratio between visual objects, sum-of-intersection-over-area (SIOA) we defined instead of the IOU metric. The second part is "occlusion group energy minimization (OGEM)" which prevents the occluded true positive tracks from false "track merging". We define each group of the occluded objects as an energy function and find an optimal hypothesis which makes the energy minimal. We evaluate the proposed tracker in benchmark datasets such as MOT15 and MOT17 which are built for multi-person tracking. An ablation study in training dataset shows that not only "track merging" and "OGEM" complement each other but also the proposed tracking method has more robust performance and less sensitive to parameters than baseline methods. Also, SIOA works better than IOU for various sizes of false positives. Experimental results show that the proposed tracker efficiently handles occlusion situations and achieves competitive performance compared to the state-of-the-art methods. Especially, our method shows the best multi-object tracking accuracy among the online and real-time executable methods.

Includes members:
  Member "online multi object tracking with dual matching attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "online multi object tracking with historical appearance matching and scene adaptive detection filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "real time multiple people tracking with deeply learned candidate selection and person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "fusion of head and full body detectors for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "real time multiple object tracking a study on the importance of speed":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "evolving boxes for fast vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spotnet self attention multi task network for object detection")
  Member "mot16 a benchmark for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "learning by tracking siamese cnn for robust target association":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiple object tracking by flowing and fusing")

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)