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: "mahotas open source software for scriptable computer vision"
  Abstract: Mahotas is a computer vision library for Python. It contains traditional image processing functionality such as filtering and morphological operations as well as more modern computer vision functions for feature computation, including interest point detection and local descriptors.   The interface is in Python, a dynamic programming language, which is appropriate for fast development, but the algorithms are implemented in C++ and are tuned for speed. The library is designed to fit in with the scientific software ecosystem in this language and can leverage the existing infrastructure developed in that language.   Mahotas is released under a liberal open source license (MIT License) and is available from http://github.com/luispedro/mahotas and from the Python Package Index (http://pypi.python.org/pypi/mahotas). Tutorials and full API documentation are available online at http://mahotas.readthedocs.org/.

Connected through hyperedge HE-1 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "deep watershed transform for instance segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "histopathologic image processing a review"):
  with members:
    - "machine learning methods for histopathological image analysis"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "rethinking the inception architecture for computer vision"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "double transfer learning for breast cancer histopathologic image classification"):
  with members:
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "improve the performance of transfer learning without fine tuning using dissimilarity based multi view learning for breast cancer histology images"):
  with members:
    - "dissimilarity based representation for radiomics applications"
    - "learning transferable architectures for scalable image recognition"
    - "aggregated residual transformations for deep neural networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "an automatic patch based approach for her 2 scoring in immunohistochemical breast cancer images using color features"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"

Connected through hyperedge HE-6 (induced by source paper "patchnet interpretable neural networks for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting cnn knowledge via an explanatory graph"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "recod titans at isic challenge 2017"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-7 (induced by source paper "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"):
  with members:
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "combinatorial energy learning for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "icon an interactive approach to train deep neural networks for segmentation of neuronal structures"):
  with members:
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

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)