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: "quality versus quantity in scientific impact"
  Abstract: Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals, and institutions. Hiring, promotion, and funding decisions increasingly rely on a variety of impact metrics that cannot disentangle quality from quantity of scientific output, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are compounded when one aggregates citation-based metrics across an entire publication record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates, in different disciplines, and in different periods of time. Here we evaluate a method based on the generation of a statistical baseline specifically tailored on the academic profile of each researcher. We demonstrate the effectiveness of the approach in decoupling the roles of quantity and quality of publications to explain how a certain level of impact is achieved. The method can be extended to simultaneously suppress any source of bias. As an illustration, we use it to capture the quality of the work of Nobel laureates irrespective of number of publications, academic age, and discipline, even when traditional metrics indicate low impact in absolute terms. The procedure is flexible enough to allow for the evaluation of, and fair comparison among, arbitrary collections of papers – scholar publication records, journals, and institutions; in fact, it extends a similar technique that was previously applied to the ranking of research units and countries in specific disciplines (Crespo, Ortuno-Orti, & Ruiz-Castillo, 2012). We further apply the methodology to almost a million scholars and over six thousand journals to measure the impact that cannot be explained by the volume of publications alone.

Connected through hyperedge HE-1 (induced by source paper "field normalization of scientometric indicators"):
  with members:
    - "relative citation ratio rcr an empirical attempt to study a new field normalized bibliometric indicator"
    - "citation analysis with microsoft academic"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "a comparison of university performance scores and ranks by mncs and fss"
    - "a farewell to the mncs and like size independent indicators"
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"

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)