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: "comparison of different methods for tissue segmentation in histopathological whole slide images"
  Abstract: Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).

Connected through hyperedge HE-1 (induced by source paper "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "neural image compression for gigapixel histopathology image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "attention based deep multiple instance learning"
    - "whole slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain invariant convolutional networks"
    - "representation learning with contrastive predictive coding"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"

Connected through hyperedge HE-3 (induced by source paper "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"):
  with members:
    - "zoom in net deep mining lesions for diabetic retinopathy detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "self critical sequence training for image captioning"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"
    - "zoom better to see clearer human and object parsing with hierarchical auto zoom net"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (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:
    - "detecting cancer metastases on gigapixel pathology images"
    - "a survey on deep learning in medical image analysis"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "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"

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)