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 "tracking road users using constraint programming"):
  Abstract: In this paper, we aim at improving the tracking of road users in urban scenes. We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem. Such an approach can solve the data association problem more efficiently than graph-based methods and can handle better the combinatorial explosion occurring when multiple frames are analyzed. Because our focus is on the data association problem, our MOT method only uses simple image features, which are the center position and color of detections for each frame. Constraints are defined on these two features and on the general MOT problem. For example, we enforce color appearance preservation over trajectories and constrain the extent of motion between frames. Filtering layers are used in order to eliminate detection candidates before using CP and to remove dummy trajectories produced by the CP solver. Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.

Includes members:
  Member "online multi object tracking with instance aware tracker and dynamic model refreshment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "tracking without bells and whistles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "multiple object tracking in urban traffic scenes with a multiclass object detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tracking in urban traffic scenes from background subtraction and object detection")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "multiple object tracking with kernelized correlation filters in urban mixed traffic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tracking in urban traffic scenes from background subtraction and object detection")
  Member "performance measures and a data set for multi target multi camera tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "high speed tracking with kernelized correlation filters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "motchallenge 2015 towards a benchmark for multi target tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")

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)