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 representation learning from electronic health records and its application on heart failure prediction"
  Abstract: Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data. Materials and Methods: The clinically meaningful representations of medical concepts and patients are the key for health analytic applications. Most of existing approaches directly construct features mapped to raw data (e.g., ICD or CPT codes), or utilize some ontology mapping such as SNOMED codes. However, none of the existing approaches leverage EHR data directly for learning such concept representation. We propose a new way to represent heterogeneous medical concepts (e.g., diagnoses, medications and procedures) based on co-occurrence patterns in longitudinal electronic health records. The intuition behind the method is to map medical concepts that are co-occuring closely in time to similar concept vectors so that their distance will be small. We also derive a simple method to construct patient vectors from the related medical concept vectors. Results: We evaluate similar medical concepts across diagnosis, medication and procedure. The results show xx% relevancy between similar pairs of medical concepts. Our proposed representation significantly improves the predictive modeling performance for onset of heart failure (HF), where classification methods (e.g. logistic regression, neural network, support vector machine and K-nearest neighbors) achieve up to 23% improvement in area under the ROC curve (AUC) using this proposed representation. Conclusion: We proposed an effective method for patient and medical concept representation learning. The resulting representation can map relevant concepts together and also improves predictive modeling performance.

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 "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "leveraging clinical time series data for prediction a cautionary tale"
    - "universal language model fine tuning for text classification"
    - "scalable and accurate deep learning for electronic health records"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "improving palliative care with deep learning"

Connected through hyperedge HE-3 (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"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-4 (induced by source paper "interpretable segmentation of medical free text records based on word embeddings"):
  with members:
    - "insights into analogy completion from the biomedical domain"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "efficient estimation of word representations in vector space"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (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"
    - "medical concept embedding with time aware attention"
    - "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"
    - "gram graph based attention model for healthcare representation learning"

Connected through hyperedge HE-6 (induced by source paper "med2meta learning representations of medical concepts with meta embeddings"):
  with members:
    - "mixed pooling multi view attention autoencoder for representation learning in healthcare"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "think globally embed locally locally linear meta embedding of words"
    - "gram graph based attention model for healthcare representation learning"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-7 (induced by source paper "mixed pooling multi view attention autoencoder for representation learning in healthcare"):
  with members:
    - "learning universal sentence representations with mean max attention autoencoder"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "gram graph based attention model for healthcare representation learning"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "analysis of big data technology for health care services"):
  with members:
    - "rethinking imagenet pre training"
    - "detecting cancer metastases on gigapixel pathology images"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "going deeper with convolutions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

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)