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: "automatic lymphocyte detection in h e images with deep neural networks"
  Abstract: Automatic detection of lymphocyte in H&E images is a necessary first step in lots of tissue image analysis algorithms. An accurate and robust automated lymphocyte detection approach is of great importance in both computer science and clinical studies. Most of the existing approaches for lymphocyte detection are based on traditional image processing algorithms and/or classic machine learning methods. In the recent years, deep learning techniques have fundamentally transformed the way that a computer interprets images and have become a matchless solution in various pattern recognition problems. In this work, we design a new deep neural network model which extends the fully convolutional network by combining the ideas in several recent techniques, such as shortcut links. Also, we design a new training scheme taking the prior knowledge about lymphocytes into consideration. The training scheme not only efficiently exploits the limited amount of free-form annotations from pathologists, but also naturally supports efficient fine-tuning. As a consequence, our model has the potential of self-improvement by leveraging the errors collected during real applications. Our experiments show that our deep neural network model achieves good performance in the images of different staining conditions or different types of tissues.

Connected through hyperedge HE-1 (induced by source paper "advanced deep convolutional neural network approaches for digital pathology image analysis a comprehensive evaluation with different use cases"):
  with members:
    - "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard"
    - "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction"
    - "automated diagnosis of lymphoma with digital pathology images using deep learning"
    - "a survey on deep learning in medical image analysis"
    - "improved inception residual convolutional neural network for object recognition"
    - "inception recurrent convolutional neural network for object recognition"

Connected through hyperedge HE-2 (induced by source paper "machine learning methods for histopathological image analysis"):
  with members:
    - "convolutional neural networks for histopathology image classification training vs using pre trained networks"
    - "detecting cancer metastases on gigapixel pathology images"
    - "the importance of stain normalization in colorectal tissue classification with convolutional networks"
    - "classification and retrieval of digital pathology scans a new dataset"
    - "a survey on deep learning in medical image analysis"
    - "neural stain style transfer learning using gan for histopathological images"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"

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)