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: "gextext disease network extraction from biomedical literature"
  Abstract: PURPOSE: We propose a fully unsupervised method to learn latent disease networks directly from unstructured biomedical text corpora. This method addresses current challenges in unsupervised knowledge extraction, such as the detection of long-range dependencies and requirements for large training corpora. METHODS: Let C be a corpus of n text chunks. Let V be a set of p disease terms occurring in the corpus. Let X indicate the occurrence of V in C. Gextext identifies disease similarities by positively correlated occurrence patterns. This information is combined to generate a graph on which geodesic distance describes dissimilarity. Diseasomes were learned by Gextext and GloVE on corpora of 100-1000 PubMed abstracts. Similarity matrix estimates were validated against biomedical semantic similarity metrics and gene profile similarity. RESULTS: Geodesic distance on Gextext-inferred diseasomes correlated inversely with external measures of semantic similarity. Gene profile similarity also correlated significant with proximity on the inferred graph. Gextext outperformed GloVE in our experiments. The information contained on the Gextext graph exceeded the explicit information content within the text. CONCLUSIONS: Gextext extracts latent relationships from unstructured text, enabling fully unsupervised modelling of diseasome graphs from PubMed abstracts.

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.DL(Digital Libraries)