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 "improving hospital mortality prediction with medical named entities and multimodal learning"):
  Abstract: Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.

Includes members:
  Member "deep ehr chronic disease prediction using medical notes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes")
  Member "clinical intervention prediction and understanding using deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what went wrong and when instance wise feature importance for time series models")
  Member "deep active learning for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "efficient vector representation for documents through corruption":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards deep federated defenses against malware in cloud ecosystems")
  Member "multi task prediction of disease onsets from longitudinal lab tests":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data")
  Member "an empirical evaluation of doc2vec with practical insights into document embedding generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "recurrent neural networks for multivariate time series with missing values":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery")
  Member "doctor ai predicting clinical events via recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")

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)