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: "benchmark of deep learning models on large healthcare mimic datasets"
  Abstract: Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the `raw' clinical time series data is used as input features to the models.

Connected through hyperedge HE-1 (induced by source paper "hierarchical target attentive diagnosis prediction in heterogeneous information networks"):
  with members:
    - "hahe hierarchical attentive heterogeneous information network embedding"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-2 (induced by source paper "blood lactate concentration prediction in critical care patients handling missing values"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"
    - "mida multiple imputation using denoising autoencoders"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-3 (induced by source paper "deep lifetime clustering"):
  with members:
    - "deep recurrent survival analysis"
    - "deep learning for patient specific kidney graft survival analysis"
    - "generative moment matching networks"
    - "adam a method for stochastic optimization"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "modeling the uncertainty in electronic health records a bayesian deep learning approach"):
  with members:
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-5 (induced by source paper "heteromed heterogeneous information network for medical diagnosis"):
  with members:
    - "automated phrase mining from massive text corpora"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "a survey of heterogeneous information network analysis"
    - "line large scale information network embedding"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for clinical time series analysis using recurrent neural networks"):
  with members:
    - "towards a universal neural network encoder for time series"
    - "transfer learning for named entity recognition with neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "dropout improves recurrent neural networks for handwriting recognition"

Connected through hyperedge HE-7 (induced by source paper "deep representation for patient visits from electronic health records"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "towards automated icd coding using deep learning"
    - "context encoders feature learning by inpainting"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "mmdf2018 workshop report"):
  with members:
    - "applications of deep learning and reinforcement learning to biological data"
    - "clinical intervention prediction and understanding using deep networks"
    - "deep learning in neural networks"

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)