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: "how much data is needed to train a medical image deep learning system to achieve necessary high accuracy"
  Abstract: The use of Convolutional Neural Networks (CNN) in natural image classification systems has produced very impressive results. Combined with the inherent nature of medical images that make them ideal for deep-learning, further application of such systems to medical image classification holds much promise. However, the usefulness and potential impact of such a system can be completely negated if it does not reach a target accuracy. In this paper, we present a study on determining the optimum size of the training data set necessary to achieve high classification accuracy with low variance in medical image classification systems. The CNN was applied to classify axial Computed Tomography (CT) images into six anatomical classes. We trained the CNN using six different sizes of training data set (5, 10, 20, 50, 100, and 200) and then tested the resulting system with a total of 6000 CT images. All images were acquired from the Massachusetts General Hospital (MGH) Picture Archiving and Communication System (PACS). Using this data, we employ the learning curve approach to predict classification accuracy at a given training sample size. Our research will present a general methodology for determining the training data set size necessary to achieve a certain target classification accuracy that can be easily applied to other problems within such systems.

Connected through hyperedge HE-1 (induced by source paper "social and governance implications of improved data efficiency"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the offense defense balance of scientific knowledge does publishing ai research reduce misuse"
    - "few shot adversarial learning of realistic neural talking head models"
    - "data efficient image recognition with contrastive predictive coding"
    - "beyond human level accuracy computational challenges in deep learning"
    - "impact of training dataset size on neural answer selection models"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "a constructive prediction of the generalization error across scales"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "impact of training dataset size on neural answer selection models"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "matchzoo a toolkit for deep text matching"
    - "neural models for information retrieval"
    - "deep learning scaling is predictable empirically"
    - "a deep relevance matching model for ad hoc retrieval"
    - "understanding deep learning requires rethinking generalization"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-4 (induced by source paper "tax2vec constructing interpretable features from taxonomies for short text classification"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "struc2vec learning node representations from structural identity"
    - "prototypical networks for few shot learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep learning in neural networks"
    - "sentiment of emojis"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "susan segment unannotated image structure using adversarial network"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-6 (induced by source paper "improving brain computer interface performance by data augmentation with conditional deep convolutional generative adversarial networks"):
  with members:
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "generative adversarial text to image synthesis"
    - "generating videos with scene dynamics"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "opening the black box of deep neural networks via information"):
  with members:
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "understanding deep learning requires rethinking generalization"
    - "text understanding from scratch"
    - "deep learning and the information bottleneck principle"
    - "speech recognition with deep recurrent neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "understanding the mechanisms of deep transfer learning for medical images"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "how transferable are features in deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.LG(Machine Learning)