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: "micro net a unified model for segmentation of various objects in microscopy images"
  Abstract: Abstract   Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "irnet instance relation network for overlapping cervical cell segmentation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "dual attention network for scene segmentation"
    - "attention is all you need"
    - "relation networks for object detection"
    - "mask r cnn"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "hover net simultaneous segmentation and classification of nuclei in multi tissue histology images"):
  with members:
    - "cia net robust nuclei instance segmentation with contour aware information aggregation"
    - "panoptic segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "methods for segmentation and classification of digital microscopy tissue images"
    - "deep learning models delineates multiple nuclear phenotypes in h e stained histology sections"
    - "a survey on deep learning in medical image analysis"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "mask r cnn"

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)