human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"):
  Abstract: Code assignment is important on many levels in the modern hospital, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious, subjective, and requires medical coders with extensive training. The objective of this study is to evaluate the performance of deep learning based systems to automatically map clinical notes to medical codes. We applied the state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks on MIMIC-III dataset. Experiments show that the deep-learning-based methods outperform other conventional machine learning methods. Our evaluations are focused on end-to-end learning methods without manually defined rules. From our evaluations, the best models are able to predict the top 10 ICD-9 codes with 69.57% F1 and 89.67% accuracy; the top 10 ICD-9 categories with 72.33% F1 and 85.88% accuracy.

Includes members:
  Member "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blank language models")
  Member "temporal convolutional networks for action segmentation and detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "condensed memory networks for clinical diagnostic inferencing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records")
  Member "retain interpretable predictive model in healthcare using reverse time attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "doctor ai predicting clinical events via recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "learning to diagnose with lstm recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tensor graph convolutional networks for text classification")
  Member "on the properties of neural machine translation encoder decoder approaches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)