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 "mpm joint representation of motion and position map for cell tracking"):
  Abstract: Conventional cell tracking methods detect multiple cellsin each frame (detection) and then associate the detec-tion results in successive time-frames (association). Mostcell tracking methods perform the association task indepen-dently from the detection task. However, there is no guar-antee of preserving coherence between these tasks, and lackof coherence may adversely affect tracking performance. Inthis paper, we propose the Motion and Position Map (MPM)that jointly represents both detection and association for notonly migration but also cell division. It guarantees coher-ence such that if a cell is detected, the corresponding mo-tion flow can always be obtained. It is a simple but powerfulmethod for multi-object tracking in dense environments. Wecompared the proposed method with current tracking meth-ods under various conditions in real biological images andfound that it outperformed the state-of-the-art (+5.2% im-provement compared to the second-best).

Includes members:
  Member "joint multi frame detection and segmentation for multi cell tracking":
    also connected through other hyperedges:
      (none)
  Member "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images")
  Member "microscopy cell segmentation via convolutional lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint multi frame detection and segmentation for multi cell tracking")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  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 "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "cross stitch networks for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "poi multiple object tracking with high performance detection and appearance feature":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refinements in motion and appearance for online multi object tracking")

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)