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: "the lost academic home institutional affiliation links in google scholar citations"
  Abstract: Purpose#R##N##R##N##R##N##R##N##R##N#Google Scholar Citations (GSC) provides an institutional affiliation link which groups together authors who belong to the same institution. The purpose of this paper is to ascertain whether this feature is able to identify and normalize all the institutions entered by the authors, and whether it is able to assign all researchers to their own institution correctly.#R##N##R##N##R##N##R##N##R##N#Design/methodology/approach#R##N##R##N##R##N##R##N##R##N#Systematic queries to GSC’s internal search box were performed under two different forms (institution name and institutional e-mail web domain) in September 2015. The whole Spanish academic system (82 institutions) was used as a test. Additionally, specific searches to companies (Google) and world-class universities were performed to identify and classify potential errors in the functioning of the feature.#R##N##R##N##R##N##R##N##R##N#Findings#R##N##R##N##R##N##R##N##R##N#Although the affiliation tool works well for most institutions, it is unable to detect all existing institutions in the database, and it is not always able to create a unique standardized entry for each institution. Additionally, it also fails to group all the authors who belong to the same institution. A wide variety of errors have been identified and classified.#R##N##R##N##R##N##R##N##R##N#Research limitations/implications#R##N##R##N##R##N##R##N##R##N#Even though the analyzed sample is good enough to empirically answer the research questions initially proposed, a more comprehensive study should be performed to calibrate the real volume of the errors.#R##N##R##N##R##N##R##N##R##N#Practical implications#R##N##R##N##R##N##R##N##R##N#The discovered affiliation link errors prevent institutions from being able to access the profiles of all their respective authors using the institutions lists offered by GSC. Additionally, it introduces a shortcoming in the navigation features of Google Scholar which may impair web user experience.#R##N##R##N##R##N##R##N##R##N#Social implications#R##N##R##N##R##N##R##N##R##N#Some institutions (mainly universities) are under-represented in the affiliation feature provided by GSC. This fact might jeopardize the visibility of institutions as well as the use of this feature in bibliometric or webometric analyses.#R##N##R##N##R##N##R##N##R##N#Originality/value#R##N##R##N##R##N##R##N##R##N#This work proves inconsistencies in the affiliation feature provided by GSC. A whole national university system is systematically analyzed and several queries have been used to reveal errors in its functioning. The completeness of the errors identified and the empirical data examined are the most exhaustive to date regarding this topic. Finally, some recommendations about how to correctly fill in the affiliation data (both for authors and institutions) and how to improve this feature are provided as well.

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)