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 "spf celltracker tracking multiple cells with strongly correlated moves using a spatial particle filter"):
  Abstract: Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in  C. elegans , we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells on the basis of shape and size only, (iii) the number of imaged cells in the several-hundred range, (iv) movements of nearly-located cells are strongly correlated, and (v) cells do not divide. We developed a tracking software suite that we call SPF-CellTracker. Incorporating dependency on the cells’ movements into the prediction model is the key for reducing the tracking errors: the cell switching and the coalescence of the tracked positions. We model the target cells’ correlated movements as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of the nuclei of  C. elegans  neurons in which approximately 120 nuclei of neurons were imaged, the proposed method demonstrated improved accuracy compared to the standard particle filter and the method developed by Tokunaga et al. (2014).

Includes members:
  Member "visualization and correction of automated segmentation tracking and lineaging from 5 d stem cell image sequences":
    also connected through other hyperedges:
      (none)
  Member "revisiting k means new algorithms via bayesian nonparametrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "infinite mixture prototypes for few shot learning")

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)