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 "validity of altmetrics data for measuring societal impact a study using data from altmetric and f1000prime"):
  Abstract: Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from very disparate sources (F1000, Altmetric, and an in-house database based on Web of Science). In the F1000 peer review system, experts attach particular tags to scientific papers which indicate whether a paper could be of interest for science or rather for other segments of society. The results show that papers with the tag "good for teaching" do achieve higher altmetric counts than papers without this tag - if the quality of the papers is controlled. At the same time, a higher citation count is shown especially by papers with a tag that is specifically scientifically oriented ("new finding"). The findings indicate that papers tailored for a readership outside the area of research should lead to societal impact. If altmetric data is to be used for the measurement of societal impact, the question arises of its normalization. In bibliometrics, citations are normalized for the papers' subject area and publication year. This study has taken a second analytic step involving a possible normalization of altmetric data. As the results show there are particular scientific topics which are of especial interest for a wide audience. Since these more or less interesting topics are not completely reflected in Thomson Reuters' journal sets, a normalization of altmetric data should not be based on the level of subject categories, but on the level of topics.

Includes members:
  Member "the substantive and practical significance of citation impact differences between institutions guidelines for the analysis of percentiles using effect sizes and confidence intervals":
    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 "real time classification of twitter trends":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers")
  Member "how to calculate the practical significance of citation impact differences an empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do altmetrics correlate with the quality of papers a large scale empirical study based on f1000prime data")
  Member "which percentile based approach should be preferred for calculating normalized citation impact values an empirical comparison of five approaches including a newly developed citation rank approach p100":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "the role of twitter in the life cycle of a scientific publication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scholarly twitter metrics")
  Member "is there currently a scientific revolution in scientometrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do altmetrics assess societal impact in a comparable way to case studies an empirical test of the convergent validity of altmetrics based on data from the uk research excellence framework ref")

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)