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: "deeppap deep convolutional networks for cervical cell classification"
  Abstract: Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation- based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

Connected through hyperedge HE-1 (induced by source paper "comparison based convolutional neural networks for cervical cell clumps detection in the limited data scenario"):
  with members:
    - "few example object detection with model communication"
    - "few shot object detection via feature reweighting"
    - "prototypical networks for few shot learning"
    - "single shot refinement neural network for object detection"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "mask r cnn"
    - "deep feature based contextual model for object detection"

Connected through hyperedge HE-2 (induced by source paper "river ice segmentation with deep learning"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning features by watching objects move"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "convolutional invasion and expansion networks for tumor growth prediction"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "unsupervised learning for physical interaction through video prediction"
    - "long term image boundary extrapolation"
    - "convolutional two stream network fusion for video action recognition"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep multi scale video prediction beyond mean square error"

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)