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: "different approaches for identifying important concepts in probabilistic biomedical text summarization"
  Abstract: Automatic text summarization tools help users in biomedical domain to acquire their intended information from various textual resources more efficiently. Some of the biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the frequency for identifying the valuable content of the input document, and considering the correlations existing between concepts may be more useful for this type of summarization. In this paper, we describe a Bayesian summarizer for biomedical text documents. The Bayesian summarizer initially maps the input text to the Unified Medical Language System (UMLS) concepts, then it selects the important ones to be used as classification features. We introduce different feature selection approaches to identify the most important concepts of the text and to select the most informative content according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian biomedical summarizer can improve the performance of summarization. We perform extensive evaluations on a corpus of scientific papers in biomedical domain. The results show that the Bayesian summarizer outperforms the biomedical summarizers that rely on the frequency of concepts, the domain-independent and baseline methods based on the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step lead to a significant increase in the performance of summarization.

Connected through hyperedge HE-1 (induced by source paper "clustering of deep contextualized representations for summarization of biomedical texts"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-2 (induced by source paper "query oriented text summarization based on hypergraph transversals"):
  with members:
    - "the minimal hitting set generation problem algorithms and computation"
    - "learning topic models going beyond svd"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "the google similarity distance"

Connected through hyperedge HE-3 (induced by source paper "text summarization in the biomedical domain"):
  with members:
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"
    - "summarization from medical documents a survey"

Connected through hyperedge HE-4 (induced by source paper "small world networks for summarization of biomedical articles"):
  with members:
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"

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)