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: "training medical image analysis systems like radiologists"
  Abstract: The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training set, and test it on a hold-out test set. This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning. In this paper, we propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets. We hypothesize that our proposed meta-training approach can be used to pre-train medical image analysis models. This hypothesis is tested on the automatic breast screening classification from DCE-MRI trained with weakly labeled datasets. The classification performance achieved by our approach is shown to be the best in the field for that application, compared to state of art baseline approaches: DenseNet, multiple instance learning and multi-task learning.

Connected through hyperedge HE-1 (induced by source paper "overcoming data limitation in medical visual question answering"):
  with members:
    - "bilinear attention networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "deep residual learning for image recognition"
    - "stacked attention networks for image question answering"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "unsupervised task design to meta train medical image classifiers"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised learning via meta learning"
    - "a survey on deep learning in medical image analysis"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"

Connected through hyperedge HE-3 (induced by source paper "multi layer domain adaptation for deep convolutional networks"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "semi supervised deep learning for fully convolutional networks"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "squeeze and excitation networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "pre and post hoc diagnosis and interpretation of malignancy from breast dce mri"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "teacher student curriculum learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "gp unet lesion detection from weak labels with a 3d regression network"

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"
    - "cased curriculum adaptive sampling for extreme data imbalance"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "long term recurrent convolutional networks for visual recognition and description"

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)