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 "the effects of research level and article type on the differences between citation metrics and f1000 recommendations"):
  Abstract: F1000 recommendations have been validated as a potential data source for research evaluation, but reasons for differences between F1000 Article Factor (FFa scores) and citations remain to be explored. By linking 28254 publications in F1000 to citations in Scopus, we investigated the effect of research level and article type on the internal consistency of assessments based on citations and FFa scores. It turns out that research level has little impact, while article type has big effect on the differences. These two measures are significantly different for two groups: non-primary research or evidence-based research publications are more highly cited rather than highly recommended, however, translational research or transformative research publications are more highly recommended by faculty members but gather relatively lower citations. This can be expected because citation activities are usually practiced by academic authors while the potential for scientific revolutions and the suitability for clinical practice of an article should be investigated from the practitioners' points of view. We conclude with a policy relevant recommendation that the application of bibliometric approaches in research evaluation procedures should include the proportion of three types of publications: evidence-based research, transformative research, and translational research. The latter two types are more suitable to be assessed through peer review.

Includes members:
  Member "liposomes versus metallic nanostructures differences in the process of knowledge translation in cancer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchy of knowledge translation from health problems to ad hoc drug design")
  Member "inter rater reliability and convergent validity of f1000prime peer review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "normalization of zero inflated data an empirical analysis of a new indicator family")
  Member "mapping knowledge translation and innovation processes in cancer drug development the case of liposomal doxorubicin":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "emerging basic clinical and translational research fronts in dental biomaterials r d")
  Member "citation analysis may severely underestimate the impact of clinical research as compared to basic research":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "should citations be field normalized in evaluative bibliometrics an empirical analysis based on propensity score matching")
  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)