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: "clinical text classification with rule based features and knowledge guided convolutional neural networks"
  Abstract: Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. Critical Steps of our method include identifying trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network with word embeddings and Unified Medical Language System (UMLS) entity embeddings. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results show that our method outperforms the state of the art methods.

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 "short text classification via knowledge powered attention with similarity matrix based cnn"):
  with members:
    - "deep short text classification with knowledge powered attention"
    - "cn probase a data driven approach for large scale chinese taxonomy construction"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "multilingual hierarchical attention networks for document classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"
    - "very deep convolutional networks for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "a neural topic attention model for medical term abbreviation disambiguation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "deep contextualized biomedical abbreviation expansion"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "medical text classification using convolutional neural networks"
    - "effective approaches to attention based neural machine translation"
    - "exploiting task oriented resources to learn word embeddings for clinical abbreviation expansion"

Connected through hyperedge HE-4 (induced by source paper "phenotyping of clinical notes with improved document classification models using contextualized neural language models"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "docbert bert for document classification"
    - "transformer xl attentive language models beyond a fixed length context"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "publicly available clinical bert embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "early prediction of acute kidney injury in critical care setting using clinical notes"):
  with members:
    - "natural language processing for ehr based computational phenotyping"

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)