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: "do mendeley readership counts help to filter highly cited wos publications better than average citation impact of journals jcs"
  Abstract: In this study, the academic status of users of scientific publications in Mendeley is explored in order to analyse the usage pattern of Mendeley users in terms of subject fields, citation and readership impact. The main focus of this study is on studying the filtering capacity of Mendeley readership counts compared to journal citation scores in detecting highly cited WoS publications. Main finding suggests a faster reception of Mendeley readerships as compared to citations across 5 major field of science. The higher correlations of scientific users with citations indicate the similarity between reading and citation behaviour among these users. It is confirmed that Mendeley readership counts filter highly cited publications (PPtop 10%) better than journal citation scores in all subject fields and by most of user types. This result reinforces the potential role that Mendeley readerships could play for informing scientific and alternative impacts.

Connected through hyperedge HE-1 (induced by source paper "readership data and research impact"):
  with members:
    - "microsoft academic a multidisciplinary comparison of citation counts with scopus and mendeley for 29 journals"
    - "do researchgate scores create ghost academic reputations"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "on full text download and citation distributions in scientific scholarly journals"
    - "national research impact indicators from mendeley readers"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "how well developed are altmetrics a cross disciplinary analysis of the presence of alternative metrics in scientific publications"
    - "user participation in an academic social networking service a survey of open group users on mendeley"

Connected through hyperedge HE-2 (induced by source paper "mendeley readership as a filtering tool to identify highly cited publications"):
  with members:
    - "ranking research institutions by the number of highly cited articles per scientist"
    - "geometric journal impact factors correcting for individual highly cited articles"
    - "networks of reader and country status an analysis of mendeley reader statistics"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "how are excellent highly cited papers defined in bibliometrics a quantitative analysis of the literature"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"
    - "how well developed are altmetrics a cross disciplinary analysis of the presence of alternative metrics in scientific publications"
    - "tweeting biomedicine an analysis of tweets and citations in the biomedical literature"

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)