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: "structured prediction models for rnn based sequence labeling in clinical text"
  Abstract: Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

Connected through hyperedge HE-2 (induced by source paper "uncertainty gated stochastic sequential model for ehr mortality prediction"):
  with members:
    - "temporal clustering invariance in irregular healthcare time series"
    - "on the variance of the adaptive learning rate and beyond"
    - "brits bidirectional recurrent imputation for time series"
    - "handling incomplete heterogeneous data using vaes"
    - "focal loss for dense object detection"
    - "estimating missing data in temporal data streams using multi directional recurrent neural networks"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-3 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "neural document summarization by jointly learning to score and select sentences"
    - "learning recurrent span representations for extractive question answering"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "multitask learning with ctc and segmental crf for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-5 (induced by source paper "time guided high order attention model of longitudinal heterogeneous healthcare data"):
  with members:
    - "medical concept embedding with time aware attention"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "learning robust features using deep learning for automatic seizure detection"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "natural language processing of mimic iii clinical notes for identifying diagnosis and procedures with neural networks"):
  with members:
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "phronesis of ai in radiology superhuman meets natural stupidity"
    - "fine tuned language models for text classification"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "universal language model fine tuning for text classification"
    - "convolutional neural networks for medical diagnosis from admission notes"

Connected through hyperedge HE-7 (induced by source paper "gnteam at 2018 n2c2 feature augmented bilstm crf for drug related entity recognition in hospital discharge summaries"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "extracting adverse drug reactions and their context using sequence labelling ensembles in tac2017"
    - "deep contextualized word representations"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "feature augmented neural networks for patient note de identification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "highrisk prediction from electronic medical records via deep attention networks"
    - "deep learning to attend to risk in icu"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "temporal relational reasoning in videos"

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.CL(Computation and Language)