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: "towards automated icd coding using deep learning"
  Abstract: International Classification of Diseases(ICD) is an authoritative health care classification system of different diseases and conditions for clinical and management purposes. Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis. We utilize character-aware neural language models to generate hidden representations of written diagnosis descriptions and ICD codes, and design an attention mechanism to address the mismatch between the numbers of descriptions and corresponding codes. Our experimental results show the strong potential of automated ICD coding from diagnosis descriptions. Our best model achieves 0.53 and 0.90 of F1 score and area under curve of receiver operating characteristic respectively. The result outperforms those achieved using character-unaware encoding method or without attention mechanism. It indicates that our proposed deep learning model can code automatically in a reasonable way and provide a framework for computer-auxiliary ICD coding.

Connected through hyperedge HE-1 (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-2 (induced by source paper "generalized zero shot icd coding"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "large scale long tailed recognition in an open world"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "dual adversarial semantics consistent network for generalized zero shot learning"
    - "explainable prediction of medical codes from clinical text"
    - "multi modal cycle consistent generalized zero shot learning"
    - "train once test anywhere zero shot learning for text classification"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "icd coding from clinical text using multi filter residual convolutional neural network"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "explainable prediction of medical codes from clinical text"
    - "multimodal machine learning for automated icd coding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "condensed memory networks for clinical diagnostic inferencing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "unsupervised annotation of phenotypic abnormalities via semantic latent representations on electronic health records"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "the deep poincar e map a novel approach for left ventricle segmentation"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "learning hierarchical representations of electronic health records for clinical outcome prediction"):
  with members:
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "assigning medical codes at the encounter level by paying attention to documents"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-7 (induced by source paper "joint embedding of words and labels for text classification"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "explainable prediction of medical codes from clinical text"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "attention over attention neural networks for reading comprehension"
    - "deconvolutional latent variable model for text sequence matching"
    - "convolutional sequence to sequence learning"
    - "deconvolutional paragraph representation learning"

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"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "regularizing neural networks by penalizing confident output distributions"

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)