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 assistant diagnosis for electronic medical record based on convolutional neural network"
  Abstract: Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Connected through hyperedge HE-1 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows"):
  with members:
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "generating steganographic text with lstms"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "image captioning with object detection and localization"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "generating steganographic text with lstms"
    - "image captioning with object detection and localization"
    - "neural responding machine for short text conversation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "natural language processing of mimic iii clinical notes for identifying diagnosis and procedures with neural networks"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "phronesis of ai in radiology superhuman meets natural stupidity"
    - "fine tuned language models for text classification"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "universal language model fine tuning for text classification"
    - "convolutional neural networks for medical diagnosis from admission notes"
    - "structured prediction models for rnn based sequence labeling in clinical text"

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)