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 "do we measure novelty when we analyze unusual combinations of cited references a validation study of bibliometric novelty indicators based on f1000prime data"):
  Abstract: Lee, Walsh, and Wang (2015) - based on Uzzi, Mukherjee, Stringer, and Jones (2013) - and Wang, Veugelers, and Stephan (2017) proposed scores based on cited references (cited journals) data which can be used to measure the novelty of papers (named as novelty scores U and W in this study). Although previous research has used novelty scores in various empirical analyses, no study has been published up to now - to the best of our knowledge - which quantitatively tested the convergent validity of novelty scores: do these scores measure what they propose to measure? Using novelty assessments by faculty members (FMs) at F1000Prime for comparison, we tested the convergent validity of the two novelty scores (U and W). FMs' assessments not only refer to the quality of biomedical papers, but also to their characteristics (by assigning certain tags to the papers): for example, are the presented findings or formulated hypotheses novel (tags "new findings" and "hypothesis")? We used these and other tags to investigate the convergent validity of both novelty scores. Our study reveals different results for the novelty scores: the results for novelty score U are mostly in agreement with previously formulated expectations. We found, for instance, that for a standard deviation (one unit) increase in novelty score U, the expected number of assignments of the "new finding" tag increase by 7.47%. The results for novelty score W, however, do not reflect convergent validity with the FMs' assessments: only the results for some tags are in agreement with the expectations. Thus, we propose - based on our results - the use of novelty score U for measuring novelty quantitatively, but question the use of novelty score W.

Includes members:
  Member "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators":
    also connected through other hyperedges:
      (none)
  Member "heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "creativity in science and the link to cited references is the creative potential of papers reflected in their cited references":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "identifying potential breakthrough publications using refined citation analyses three related explorative approaches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "research assessment by percentile based double rank analysis")
  Member "coauthorship networks a directed network approach considering the order and number of coauthors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "examining scientific writing styles from the perspective of linguistic complexity")
  Member "the effects of research level and article type on the differences between citation metrics and f1000 recommendations":
    also connected through other hyperedges:
      (none)
  Member "the validation of advanced bibliometric indicators through peer assessments a comparative study using data from incites and f1000":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators")

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)