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: "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
  Abstract: When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies.

Connected through hyperedge HE-1 (induced by source paper "an alarm system for segmentation algorithm based on shape model"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "robust image segmentation quality assessment without ground truth"):
  with members:
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "identifying and categorizing anomalies in retinal imaging data"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning free iris segmentation revisited a first step toward fast volumetric operation over video samples"):
  with members:
    - "unconstrained iris segmentation using convolutional neural networks"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "data driven segmentation of post mortem iris images"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a fine grain error map prediction and segmentation quality assessment framework for whole heart segmentation"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "leveraging uncertainty estimates for predicting segmentation quality"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "detecting cancer metastases on gigapixel pathology images"
    - "a survey on deep learning in medical image analysis"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "a volumetric convolutional neural network for brain tumor segmentation"):
  with members:
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "multimodal mri brain tumor segmentation using random forests with features learned from fully convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "on calibration of modern neural networks"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "densely connected convolutional networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "domain adaptation for mri organ segmentation using reverse classification accuracy"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "transfer learning for domain adaptation in mri application in brain lesion segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "cnn features off the shelf an astounding baseline for recognition"

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)