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: "multi label classification of patient notes a case study on icd code assignment"
  Abstract: In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows easier error analysis, and suggests future directions for improvement.

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 "graph driven generative models for heterogeneous multi task learning"):
  with members:
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "improving textual network learning with variational homophilic embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "graph convolutional networks for text classification"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"

Connected through hyperedge HE-3 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-4 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring"):
  with members:
    - "learning to write notes in electronic health records"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "building a conversational agent overnight with dialogue self play"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic answer networks for squad 2 0"
    - "quac question answering in context"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "a multi label classification method using a hierarchical and transparent representation for paper reviewer recommendation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "large scale multi label text classification revisiting neural networks"
    - "a framework for optimizing paper matching"

Connected through hyperedge HE-7 (induced by source paper "ml net multi label classification of biomedical texts with deep neural networks"):
  with members:
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "improving pairwise ranking for multi label image classification"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"

Connected through hyperedge HE-8 (induced by source paper "distilled wasserstein learning for word embedding and topic modeling"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "progressive neural networks"
    - "topic compositional neural language model"
    - "regularizing neural networks by penalizing confident output distributions"
    - "jointly learning word embeddings and latent topics"

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)