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 "gextext unsupervised knowledge modelling in biomedical literature"):
  Abstract: PURPOSE: Literature review is a complex task, requiring the expert analysis of unstructured data. Computational automation of this process presents a valuable opportunity for high throughput knowledge extraction and meta analysis. Currently available methods are limited to the detection of explicit and short-context relationships. We address this challenge with Gextext, which extracts a knowledge graph of latent relationships directly from unstructured text. METHODS: Let C be a corpus of n text chunks. Let V_target be a set of query terms and V_random a random selection of terms in C. Let X indicate the occurrence of V_target and V_random in C. Gextext learns a graph G(V,E) by correlation thresholding on the covariance matrix of X, where thresholds are estimated by the correlations with randomly selected terms. Gextext was benchmarked against GloVE in tasks where embedding distance matrices were correlated against real world similarity matrices. A general corpus was generated from 5,000 randomly selected Wikipedia articles and a biomedical corpus from 961 research papers on stroke. RESULTS: Embeddings generated by Gextext preserved relative geographical distances between countries (Gextext: rho = 0.255, p < 2.22e-16; GloVE: rho = 0.086, p = 1.859e-09) and capital cities (Gextext: rho = 0.282, p < 2.22e-16 ; Glove: rho = 0.093, p = 8.0805e-11). Gextext embeddings organised drug names by shared target (Gextext: rho = 0.456, p < 2.22e-16; GloVE: rho = 0.091, p = 0.00087) and stroke phenotypes by body system (Gextext: rho = 0.446, p < 2.22e-16; GloVE: rho = 0.129, p = 1.7464e-11). CONCLUSIONS: Gextext extracts latent relationships from unstructured text, enabling fully unsupervised automation of the literature review process.

Includes members:
  Member "constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge based biomedical data science 2019")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")

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.DL(Digital Libraries)