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: "adversarial active learning for deep networks a margin based approach"
  Abstract: We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous active learning strategies scalable for deep networks were mostly based on uncertain sample selection. In this work, we focus on examples lying close to the decision boundary. Based on theoretical works on margin theory for active learning, we know that such examples may help to considerably decrease the number of annotations. While measuring the exact distance to the decision boundaries is intractable, we propose to rely on adversarial examples. We do not consider anymore them as a threat instead we exploit the information they provide on the distribution of the input space in order to approximate the distance to decision boundaries. We demonstrate empirically that adversarial active queries yield faster convergence of CNNs trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.

Connected through hyperedge HE-1 (induced by source paper "information condensing active learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "understanding the limitations of variational mutual information estimators"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "discriminative active learning"
    - "emnist an extension of mnist to handwritten letters"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-3 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "active image synthesis for efficient labeling"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "generative invertible networks gin pathophysiology interpretable feature mapping and virtual patient generation"
    - "generative adversarial active learning"
    - "towards deeper understanding of variational autoencoding models"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-5 (induced by source paper "sampling bias in deep active classification an empirical study"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "practical obstacles to deploying active learning"
    - "discriminative active learning"
    - "an empirical study of example forgetting during deep neural network learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "when will gradient methods converge to max margin classifier under relu models"

Connected through hyperedge HE-6 (induced by source paper "bayesian generative active deep learning"):
  with members:
    - "discriminative active learning"
    - "scalable and accurate deep learning for electronic health records"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "generative adversarial active learning"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"
    - "a bayesian data augmentation approach for learning deep models"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "deep active learning unified and principled method for query and training"):
  with members:
    - "bayesian generative active deep learning"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "variational adversarial active learning"
    - "a principled approach for learning task similarity in multitask learning"
    - "adversarial sampling for active learning"
    - "generative adversarial active learning"
    - "learning algorithms for active learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "libact pool based active learning in python"
    - "determinantal point processes for mini batch diversification"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"

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)