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 "like for like bibliometric substitutes for peer review advantages and limits of indicators calculated from the ep index"):
  Abstract: The use of bibliometric indicators would simplify research assessments. The 2014 Research Excellence Framework (REF) is a peer review assessment of UK universities, whose results can be taken as benchmarks for bibliometric indicators. In this study we use the REF results to investigate whether the ep index and a top percentile of most cited papers could substitute for peer review. The probability that a random university's paper reaches a certain top percentile in the global distribution of papers is a power of the ep index, which can be calculated from the citation-based distribution of university's papers in global top percentiles. Making use of the ep index in each university and research area, we calculated the ratios between the percentage of 4-star-rated outputs in REF and the percentages of papers in global top percentiles. Then, we fixed the assessment percentile so that the mean ratio between these two indicators across universities is 1.0. This method was applied to four units of assessment in REF: Chemistry, Economics & Econometrics joined to Business & Management Studies, and Physics. Some relevant deviations from the 1.0 ratio could be explained by the evaluation procedure in REF or by the characteristics of the research field; other deviations need specific studies by experts in the research area. The present results indicate that in many research areas the substitution of a top percentile indicator for peer review is possible. However, this substitution cannot be made straightforwardly; more research is needed to establish the conditions of the bibliometric assessment.

Includes members:
  Member "technological research in the eu is less efficient than the world average eu research policy risks europeans future":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "might europe one day again be a global scientific powerhouse analysis of erc publications suggests it will not be possible without changes in research policy")
  Member "a review of theory and practice in scientometrics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data")
  Member "a review of the literature on citation impact indicators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data")
  Member "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts":
    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 "a systematic empirical comparison of different approaches for normalizing citation impact indicators":
    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")
  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-6 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "on the calculation of percentile based bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")

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)