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: "comparing rule based and deep learning models for patient phenotyping"
  Abstract: Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. #R##N#Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. #R##N#Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. #R##N#Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.

Connected through hyperedge HE-1 (induced by source paper "natural language processing for ehr based computational phenotyping"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients"
    - "toward mention detection robustness with recurrent neural networks"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "combining neural networks and log linear models to improve relation extraction"
    - "how essential are unstructured clinical narratives and information fusion to clinical trial recruitment"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"

Connected through hyperedge HE-2 (induced by source paper "learning tasks for multitask learning heterogenous patient populations in the icu"):
  with members:
    - "the use of autoencoders for discovering patient phenotypes"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "personalized risk scoring for critical care patients using mixtures of gaussian process experts"
    - "recurrent neural networks for multivariate time series with missing values"
    - "show attend and tell neural image caption generation with visual attention"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deeptag inferring all cause diagnoses from clinical notes in under resourced medical domain"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "wavenet a generative model for raw audio"
    - "a method for modeling co occurrence propensity of clinical codes with application to icd 10 pcs auto coding"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-4 (induced by source paper "characterizing design patterns of ehr driven phenotype extraction algorithms"):
  with members:
    - "natural language processing for ehr based computational phenotyping"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "mit at semeval 2017 task 10 relation extraction with convolutional neural networks"):
  with members:
    - "neural networks for joint sentence classification in medical paper abstracts"
    - "natural language processing almost from scratch"

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)