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: "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
  Abstract: Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a pre-requisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottom-up deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progressive multi-path scheme, which more reliably merges outputs from different network stages. The result is a deep model able to produce finer detailed masks, which we call progressive holistically-nested networks (P-HNNs). Using extensive cross-validation, our method is tested on a multi-institutional dataset comprising 929 CT scans (848 publicly available), of pathological lungs, reporting mean dice scores of 0.985 and demonstrating significant qualitative and quantitative improvements over state-of-the art approaches.

Connected through hyperedge HE-1 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep label distribution learning with label ambiguity"
    - "detecting cancer metastases on gigapixel pathology images"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "deep learning assessment of tumor proliferation in breast cancer histological images"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "lgan lung segmentation in ct scans using generative adversarial network"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "semantic segmentation using adversarial networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep end2end voxel2voxel prediction"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "automatic segmentation of pulmonary lobes using a progressive dense v network"):
  with members:
    - "pathological pulmonary lobe segmentation from ct images using progressive holistically nested neural networks and random walker"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "semi supervised multi organ segmentation via deep multi planar co training"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "deep co training for semi supervised image recognition"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "data distillation towards omni supervised learning"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"

Connected through hyperedge HE-5 (induced by source paper "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"):
  with members:
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"):
  with members:
    - "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"):
  with members:
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep mr to ct synthesis using unpaired data"
    - "medical image synthesis with context aware generative adversarial networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "joint vertebrae identification and localization in spinal ct images by combining short and long range contextual information"):
  with members:
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"
    - "deep multi task representation learning a tensor factorisation approach"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "fully convolutional networks for semantic 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)