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: "improving whole slide segmentation through visual context a systematic study"
  Abstract: While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.

Connected through hyperedge HE-1 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification"):
  with members:
    - "attentive single tasking of multiple tasks"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "on calibration of modern neural networks"
    - "an analysis of scale invariance in object detection snip"
    - "a survey on deep learning in medical image analysis"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "adaptive weighting multi field of view cnn for semantic segmentation in pathology"):
  with members:
    - "autofocus layer for semantic segmentation"
    - "switching convolutional neural network for crowd counting"
    - "squeeze and excitation networks"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "learning convolutional networks for content weighted image compression"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-3 (induced by source paper "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "multi resolution networks for semantic segmentation in whole slide images"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "attention is all you need"
    - "deep learning applied to nlp"
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "ca refinenet a dual input wsi image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "deep learning applied to nlp"
    - "convolutional sequence to sequence learning"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

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)