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 "using clinical notes with time series data for icu management"):
  Abstract: Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at \url{this https URL}.

Includes members:
  Member "clinicalbert modeling clinical notes and predicting hospital readmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "explainable prediction of medical codes from clinical text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "raim recurrent attentive and intensive model of multimodal patient monitoring data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what went wrong and when instance wise feature importance for time series models")
  Member "learning tasks for multitask learning heterogenous patient populations in the icu":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairface face attribute dataset for balanced race gender and age")
  Member "improving hospital mortality prediction with medical named entities and multimodal learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "assessment of amazon comprehend medical medication information extraction")
  Member "biosentvec creating sentence embeddings for biomedical texts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology")
  Member "rationale augmented convolutional neural networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models")
  Member "explain images with multimodal recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")

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)