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: "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
  Abstract: Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring. #R##N##R##N#Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues. Available from: https://www.researchgate.net/publication/317087832_Her2_Challenge_Contest_A_Detailed_Assessment_of_Automated_Her2_Scoring_Algorithms_in_Whole_Slide_Images_of_Breast_Cancer_Tissues [accessed Jul 31, 2017].

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-2 (induced by source paper "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"):
  with members:
    - "dynamic routing between capsules"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"
    - "gland segmentation in colon histology images the glas challenge contest"

Connected through hyperedge HE-3 (induced by source paper "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "data efficient image recognition with contrastive predictive coding"
    - "cluster based learning from weakly labeled bags in digital pathology"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "classification of breast cancer histology using deep learning"
    - "smooth loss functions for deep top k classification"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "deep learning for whole slide image analysis an overview"):
  with members:
    - "learning where to see a novel attention model for automated immunohistochemical scoring"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "classification and disease localization in histopathology using only global labels a weakly supervised approach"
    - "cancer metastasis detection with neural conditional random field"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "learning where to see a novel attention model for automated immunohistochemical scoring"):
  with members:
    - "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"
    - "fully convolutional attention networks for fine grained recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "active object localization with deep reinforcement learning"
    - "going deeper with convolutions"
    - "recurrent models of visual attention"

Connected through hyperedge HE-6 (induced by source paper "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"):
  with members:
    - "learning where to see a novel attention model for automated immunohistochemical scoring"
    - "dynamic routing between capsules"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "dissimilarity based sparse subset selection"

Connected through hyperedge HE-7 (induced by source paper "an automatic patch based approach for her 2 scoring in immunohistochemical breast cancer images using color features"):
  with members:
    - "mahotas open source software for scriptable computer vision"

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)