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: "a bayesian approach to biomedical text summarization"
  Abstract: Automatic text summarization tools help biomedical researchers and clinicians to acquire intended information from various textual resources in shorter time and with less effort. 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 can be more useful for this type of summarization. In this paper, we propose a biomedical text summarization method based on the na\"ive Bayes classifier. The input text is mapped to the Unified Medical Language System (UMLS) concepts, and the important ones are selected to be used as the classification features. We introduce different feature selection approaches to identify the most important concepts of the text and select the most informative content according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, our Bayesian biomedical summarizer can improve the performance of summarization. An extensive evaluation was performed on a collection of scientific papers in biomedical domain. The results show that our proposed method 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 yield a desirable summarization performance.

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)