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: "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
  Abstract: The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network namely ehrGAN, which can provide plausible labeled EHR data by mimicking real patient records, to augment the training dataset in a semi-supervised learning manner. We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance. Experiments on two real healthcare datasets demonstrate that our proposed framework produces realistic data samples and achieves significant improvements on classification tasks with the generated data over several stat-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "transfer learning for time series classification"

Connected through hyperedge HE-2 (induced by source paper "augmenting physiological time series data a case study for sleep apnea detection"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "bagan data augmentation with balancing gan"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "multi generator generative adversarial nets"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative multi adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "conan complementary pattern augmentation for rare disease detection"):
  with members:
    - "learning from positive and unlabeled data a survey"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "representation learning with autoencoders for electronic health records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a survey on deep learning in medical image analysis"
    - "adversarial images for variational autoencoders"
    - "deepqa improving the estimation of single protein model quality with deep belief networks"
    - "safs a deep feature selection approach for precision medicine"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "modeling tabular data using conditional gan"):
  with members:
    - "data synthesis based on generative adversarial networks"
    - "generative adversarial networks for electronic health records a framework for exploring and evaluating methods for predicting drug induced laboratory test trajectories"
    - "pacgan the power of two samples in generative adversarial networks"
    - "improved training of wasserstein gans"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "synthesizing tabular data using generative adversarial networks"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "correlated discrete data generation using adversarial training"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "began boundary equilibrium generative adversarial networks"
    - "mida multiple imputation using denoising autoencoders"
    - "generative adversarial networks for electronic health records a framework for exploring and evaluating methods for predicting drug induced laboratory test trajectories"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "transfer learning for time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "towards a universal neural network encoder for time series"
    - "domain adaptation for visual applications a comprehensive survey"
    - "deep learning for time series analysis"
    - "spatio temporal neural networks for space time series forecasting and relations discovery"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-8 (induced by source paper "time discounting convolution for event sequences with ambiguous timestamps"):
  with members:
    - "modeling the intensity function of point process via recurrent neural networks"
    - "unsupervised representation learning of structured radio communication signals"
    - "recurrent neural networks for multivariate time series with missing values"
    - "conditional random fields as recurrent neural networks"
    - "neural machine translation of rare words with subword units"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning dynamic boltzmann machines with spike timing dependent plasticity"
    - "adam a method for stochastic optimization"

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)