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: "deep convolutional neural networks for microscopy based point of care diagnostics"
  Abstract: Point of care diagnostics using microscopy and computer vision methods have been applied to a number of practical problems, and are particularly relevant to low-income, high disease burden areas. However, this is subject to the limitations in sensitivity and specificity of the computer vision methods used. In general, deep learning has recently revolutionised the field of computer vision, in some cases surpassing human performance for other object recognition tasks. In this paper, we evaluate the performance of deep convolutional neural networks on three different microscopy tasks: diagnosis of malaria in thick blood smears, tuberculosis in sputum samples, and intestinal parasite eggs in stool samples. In all cases accuracy is very high and substantially better than an alternative approach more representative of traditional medical imaging techniques.

Connected through hyperedge HE-1 (induced by source paper "leishmaniasis parasite segmentation and classification using deep learning"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks that teach microscopes how to image"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "learning sensor multiplexing design through back propagation"
    - "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"
    - "pixel recurrent neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "why m heads are better than one training a diverse ensemble of deep networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for medical image processing overview challenges and future"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
    - "cascaded neural networks with selective classifiers and its evaluation using lung x ray ct images"

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)