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: "convolutional neural networks for medical diagnosis from admission notes"
  Abstract: $\textbf{Objective}$ Develop an automatic diagnostic system which only uses textual admission information from Electronic Health Records (EHRs) and assist clinicians with a timely and statistically proved decision tool. The hope is that the tool can be used to reduce mis-diagnosis. #R##N#$\textbf{Materials and Methods}$ We use the real-world clinical notes from MIMIC-III, a freely available dataset consisting of clinical data of more than forty thousand patients who stayed in intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional Neural Network model to learn semantic features from unstructured textual input and automatically predict primary discharge diagnosis. #R##N#$\textbf{Results}$ The proposed model achieved an overall 96.11% accuracy and 80.48% weighted F1 score values on 10 most frequent disease classes, significantly outperforming four strong baseline models by at least 12.7% in weighted F1 score. #R##N#$\textbf{Discussion}$ Experimental results imply that the CNN model is suitable for supporting diagnosis decision making in the presence of complex, noisy and unstructured clinical data while at the same time using fewer layers and parameters that other traditional Deep Network models. #R##N#$\textbf{Conclusion}$ Our model demonstrated capability of representing complex medical meaningful features from unstructured clinical notes and prediction power for commonly misdiagnosed frequent diseases. It can use easily adopted in clinical setting to provide timely and statistically proved decision support. #R##N#$\textbf{Keywords}$ Convolutional neural network, text classification, discharge diagnosis prediction, admission information from EHRs.

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 "cross lingual data transformation and combination for text classification"):
  with members:
    - "learning word vectors for 157 languages"
    - "revisiting the importance of encoding logic rules in sentiment classification"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "social emotion mining techniques for facebook posts reaction prediction"
    - "fasttext zip compressing text classification models"
    - "one model to learn them all"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (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"
    - "structured prediction models for rnn based sequence labeling in clinical text"

Connected through hyperedge HE-4 (induced by source paper "patient risk assessment and warning symptom detection using deep attention based neural networks"):
  with members:
    - "very deep convolutional networks for text classification"
    - "end to end memory networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a c lstm neural network for text classification"
    - "convolutional neural networks for sentence classification"
    - "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.CL(Computation and Language)