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: "exploiting task oriented resources to learn word embeddings for clinical abbreviation expansion"
  Abstract: In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach ‐ exploiting taskoriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human 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 "surf at mediqa 2019 improving performance of natural language inference in the clinical domain by adopting pre trained language model"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "dr quad at mediqa 2019 towards textual inference and question entailment using contextualized representations"
    - "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"
    - "multi task deep neural networks for natural language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"

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

Connected through hyperedge HE-4 (induced by source paper "using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora"):
  with members:
    - "a survey of word embeddings evaluation methods"
    - "drug drug interaction extraction via recurrent neural network with multiple attention layers"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "highrisk prediction from electronic medical records via deep attention networks"
    - "deep learning to attend to risk in icu"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-6 (induced by source paper "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"):
  with members:
    - "a short review of ethical challenges in clinical natural language processing"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "bidirectional recurrent neural networks for medical event detection in electronic health records"
    - "de identification of patient notes with recurrent neural networks"
    - "brundlefly at semeval 2016 task 12 recurrent neural networks vs joint inference for clinical temporal information extraction"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "clinical concept embeddings learned from massive sources of multimodal medical data"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "efficient estimation of word representations in vector space"
    - "a short introduction to nile"

Connected through hyperedge HE-8 (induced by source paper "unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "learning algorithms for keyphrase extraction"

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)