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: "sliderunner a tool for massive cell annotations in whole slide images"
  Abstract: Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. #R##N#To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.

Connected through hyperedge HE-1 (induced by source paper "field of interest prediction for computer aided mitotic count"):
  with members:
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning new tricks from old dogs inter species inter tissue domain adaptation for mitotic figure assessment"):
  with members:
    - "super convergence very fast training of residual networks using large learning rates"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "field of interest proposal for augmented mitotic cell count comparison of two convolutional networks"):
  with members:
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "augmented mitotic cell count using field of interest proposal"
    - "cnn based segmentation of medical imaging data"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"

Connected through hyperedge HE-4 (induced by source paper "augmented mitotic cell count using field of interest proposal"):
  with members:
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "u net convolutional networks for biomedical image segmentation"

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)