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: "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators"
  Abstract: Recently, the integrated impact indicator (I3) indicator was introduced where citations are weighted in accordance with the percentile rank class of each publication in a set of publications. I3 can also be used as a field-normalized indicator. Field-normalization is common practice in bibliometrics, especially when institutions and countries are compared. Publication and citation practices are so different among fields that citation impact is normalized for cross-field comparisons. In this study, we test the ability of the indicator to discriminate between quality levels of papers as defined by Faculty members at F1000Prime. F1000Prime is a post-publication peer review system for assessing papers in the biomedical area. Thus, we test the convergent validity of I3 (in this study, we test I3/N - the size-independent variant of I3 where I3 is divided by the number of papers) using assessments by peers as baseline and compare its validity with several other (field-normalized) indicators: the mean-normalized citation score (MNCS), relative-citation ratio (RCR), citation score normalized by cited references (CSNCR), characteristic scores and scales (CSS), source-normalized citation score (SNCS), citation percentile, and proportion of papers which belong to the x% most frequently cited papers (PPtop x%). The results show that the PPtop 1% indicator discriminates best among different quality levels. I3 performs similar as (slightly better than) most of the other field-normalized indicators. Thus, the results point out that the indicator could be a valuable alternative to other indicators in bibliometrics.

Connected through hyperedge HE-1 (induced by source paper "do we measure novelty when we analyze unusual combinations of cited references a validation study of bibliometric novelty indicators based on f1000prime data"):
  with members:
    - "heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation"
    - "creativity in science and the link to cited references is the creative potential of papers reflected in their cited references"
    - "identifying potential breakthrough publications using refined citation analyses three related explorative approaches"
    - "coauthorship networks a directed network approach considering the order and number of coauthors"
    - "the effects of research level and article type on the differences between citation metrics and f1000 recommendations"
    - "the validation of advanced bibliometric indicators through peer assessments a comparative study using data from incites and f1000"

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)