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: "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
  Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Connected through hyperedge HE-1 (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical image segmentation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge"
    - "boundary aware feature propagation for scene segmentation"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"
    - "learning from weak and noisy labels for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "random bundle brain metastases segmentation ensembling through annotation randomization"):
  with members:
    - "wide residual networks"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-4 (induced by source paper "multi scale guided attention for medical image segmentation"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"
    - "an attention model for group level emotion recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "mask r cnn with pyramid attention network for scene text detection"

Connected through hyperedge HE-5 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "harmonious attention network for person re identification"
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "compnet complementary segmentation network for brain mri extraction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "no new net"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "modelhub ai dissemination platform for deep learning models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "dlhub model and data serving for science"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"
    - "tensorflow serving flexible high performance ml serving"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "sgd with hardness weighted sampling for distributionally robust deep learning"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "on the convergence rate of training recurrent neural networks"
    - "niftynet a deep learning platform for medical imaging"
    - "a convergence theory for deep learning via over parameterization"
    - "training region based object detectors with online hard example mining"
    - "wide residual networks"
    - "minimizing the maximal loss how and why"

Connected through hyperedge HE-8 (induced by source paper "3d dilated multi fiber network for real time brain tumor segmentation in mri"):
  with members:
    - "multi fiber networks for video recognition"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "no new net"
    - "autofocus layer for semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "brain tumor segmentation with deep neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

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)