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: "medical concept embedding with time aware attention"
  Abstract: Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal nature of EMR data. On the one hand, two consecutive medical concepts do not indicate they are temporally close, but the correlations between them can be revealed by the time gap. On the other hand, the temporal scopes of medical concepts often vary greatly (e.g., \textit{common cold} and \textit{diabetes}). In this paper, we propose to incorporate the temporal information to embed medical codes. Based on the Continuous Bag-of-Words model, we employ the attention mechanism to learn a "soft" time-aware context window for each medical concept. Experiments on public and proprietary datasets through clustering and nearest neighbour search tasks demonstrate the effectiveness of our model, showing that it outperforms five state-of-the-art baselines.

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"
    - "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"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "reinforced mnemonic reader for machine comprehension"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "bioconceptvec creating and evaluating literature based biomedical concept embeddings on a large scale"):
  with members:
    - "relation extraction between the clinical entities based on the shortest dependency path based lstm"
    - "biomedical mention disambiguation using a deep learning approach"
    - "reflex flexible framework for relation extraction in multiple domains"
    - "deep learning with sentence embeddings pre trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records"
    - "concept based embeddings for natural language processing"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "clinical concept embeddings learned from massive sources of multimodal medical data"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "taper time aware patient ehr representation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "dynamic feature acquisition using denoising autoencoders"
    - "publicly available clinical bert embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "unsupervised representation for ehr signals and codes as patient status vector"):
  with members:
    - "improving clinical predictions through unsupervised time series representation learning"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "ecg heartbeat classification a deep transferable representation"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "the use of autoencoders for discovering patient phenotypes"
    - "gram graph based attention model for healthcare representation learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "medgraph structural and temporal representation learning of electronic medical records"):
  with members:
    - "gaussian embedding of large scale attributed graphs"
    - "learning the graphical structure of electronic health records with graph convolutional transformer"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "xgboost a scalable tree boosting system"
    - "semi supervised classification with graph convolutional networks"
    - "gram graph based attention model for healthcare representation learning"

Connected through hyperedge HE-7 (induced by source paper "inpatient2vec medical representation learning for inpatients"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "gram graph based attention model for healthcare representation learning"

Connected through hyperedge HE-8 (induced by source paper "benchmarking deep learning architectures for predicting readmission to the icu and describing patients at risk"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "deep ehr chronic disease prediction using medical notes"
    - "scalable and accurate deep learning for electronic health records"
    - "discrete event continuous time rnns"
    - "recurrent neural networks for multivariate time series with missing values"
    - "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.CL(Computation and Language)