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: "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
  Abstract: Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.

Connected through hyperedge HE-1 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction"):
  with members:
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a bottom up approach for pancreas segmentation using cascaded superpixels and deep image patch labeling"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "outlier guided optimization of abdominal segmentation"):
  with members:
    - "splenomegaly segmentation on multi modal mri using deep convolutional networks"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "unsupervised body part regression via spatially self ordering convolutional neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "adversarial attacks and defences competition"
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"

Connected through hyperedge HE-4 (induced by source paper "understanding adversarial attacks on deep learning based medical image analysis systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting and improving adversarial robustness with neuron sensitivity"
    - "towards noise robust neural networks via progressive adversarial training"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "black box adversarial attacks on video recognition models"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-5 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"

Connected through hyperedge HE-6 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "scalable neural architecture search for 3d medical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "thickened 2d networks for efficient 3d medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "long term feature banks for detailed video understanding"
    - "compact generalized non local network"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "differentiable learning to normalize via switchable normalization"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"

Connected through hyperedge HE-8 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unet a nested u net architecture for medical image segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"

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)