human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "jansen midas a multi level photomicrograph segmentation software based on isotropic undecimated wavelets"
  Abstract: Image segmentation, the process of separating the elements within an image, is frequently used for obtaining information from photomicrographs. However, segmentation methods should be used with reservations: incorrect segmentation can mislead when interpreting regions of interest (ROI), thus decreasing the success rate of additional procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to address the photomicrograph segmentation deficiency on general tools. These methods gave rise to Jansen-MIDAS, an open-source software which a scientist can use to obtain a multi-level threshold segmentation of his/hers photomicrographs. This software is presented in two versions: a text-based version, for GNU Octave, and a graphical user interface (GUI) version, for MathWorks MATLAB. It can be used to process several types of images, becoming a reliable alternative to the scientist.

Connected through hyperedge HE-1 (induced by source paper "skeletracks automatic separation of overlapping fission tracks in apatite and muscovite using image processing"):
  with members:
    - "scikit image image processing in python"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-2 (induced by source paper "segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers"):
  with members:
    - "imagej2 imagej for the next generation of scientific image data"
    - "scikit image image processing in python"
    - "an automatic method for segmentation of fission tracks in epidote crystal photomicrographs"
    - "the numpy array a structure for efficient numerical computation"

Question: Please tell me which class the center node 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)