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: "cased curriculum adaptive sampling for extreme data imbalance"
  Abstract: We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.

Connected through hyperedge HE-1 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "attention based deep multiple instance learning"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "towards radiologist level cancer risk assessment in ct lung screening using deep learning"
    - "s4nd single shot single scale lung nodule detection"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks"):
  with members:
    - "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "spinal cord gray matter segmentation using deep dilated convolutions"
    - "domain adaptation for mri organ segmentation using reverse classification accuracy"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "semi supervised deep learning for fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"):
  with members:
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "curriculum learning by transfer learning theory and experiments with deep networks"):
  with members:
    - "automated curriculum learning for neural networks"
    - "training region based object detectors with online hard example mining"
    - "basic level categorization facilitates visual object recognition"
    - "learning structured inference neural networks with label relations"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "compressing neural networks with the hashing trick"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning"):
  with members:
    - "learning based quality control for cardiac mr images"
    - "a machine learning framework for automatic reference free quality assessment in mri"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "deep learning using k space based data augmentation for automated cardiac mr motion artefact detection"
    - "nima neural image assessment"
    - "training medical image analysis systems like radiologists"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-6 (induced by source paper "integrating feature and image pyramid a lung nodule detector learned in curriculum fashion"):
  with members:
    - "automated curriculum learning for neural networks"
    - "focal loss for dense object detection"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "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)