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: "a unified framework for tumor proliferation score prediction in breast histopathology"
  Abstract: We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based score prediction and 0.652 F1-score in mitosis detection. On Spearman's correlation coefficient, which evaluates predictive accuracy on the molecular data based score, the system obtained 0.6171. Our approach won first place in all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which is MICCAI grand challenge.

Connected through hyperedge HE-1 (induced by source paper "deep object detection based mitosis analysis in breast cancer histopathological images"):
  with members:
    - "multi stream faster rcnn for mitosis counting in breast cancer images"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "processing megapixel images with deep attention sampling models"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "learning to zoom a saliency based sampling layer for neural networks"

Connected through hyperedge HE-3 (induced by source paper "landscape of big medical data a pragmatic survey on prioritized tasks"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "deep activity recognition models with triaxial accelerometers"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

Connected through hyperedge HE-4 (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"
    - "sliderunner a tool for massive cell annotations in whole slide images"
    - "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-5 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-6 (induced by source paper "leveraging unlabeled whole slide images for mitosis detection"):
  with members:
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "data distillation towards omni supervised learning"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "deep residual learning for image recognition"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"):
  with members:
    - "comparison of different methods for tissue segmentation in histopathological whole slide images"
    - "detecting cancer metastases on gigapixel pathology images"
    - "a survey on deep learning in medical image analysis"
    - "theano a python framework for fast computation of mathematical expressions"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-8 (induced by source paper "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

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)