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: "adversarial deep structured nets for mass segmentation from mammograms"
  Abstract: Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{this https URL}

Connected through hyperedge HE-1 (induced by source paper "end to end face parsing via interlinked convolutional neural networks"):
  with members:
    - "face parsing with roi tanh warping"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "face parsing via a fully convolutional continuous crf neural network"
    - "mask r cnn"
    - "face alignment in the wild a survey"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a cnn cascade for landmark guided semantic part segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "darnet deep active ray network for building segmentation"
    - "mesh r cnn"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "improved breast mass segmentation in mammograms with conditional residual u net"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-4 (induced by source paper "landscape of big medical data a pragmatic survey on prioritized tasks"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a survey on deep learning in medical image analysis"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep activity recognition models with triaxial accelerometers"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

Connected through hyperedge HE-5 (induced by source paper "fuzzy semantic segmentation of breast ultrasound image with breast anatomy constraints"):
  with members:
    - "an end to end deep learning histochemical scoring system for breast cancer tma"
    - "a benchmark for breast ultrasound image segmentation busis"
    - "automatic breast ultrasound image segmentation a survey"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic image segmentation via deep parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "squeeze and excitation networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"

Connected through hyperedge HE-7 (induced by source paper "a deep dual path network for improved mammogram image processing"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "adapting mask rcnn for automatic nucleus segmentation"):
  with members:
    - "yolov3 an incremental improvement"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "focal loss for dense object detection"
    - "perceptual losses for real time style transfer and super resolution"
    - "ssd single shot multibox detector"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "feature pyramid networks for object detection"

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)