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 "refrain from adopting the combination of citation and journal metrics to grade publications as used in the italian national research assessment exercise vqr 2011 2014"):
  Abstract: The prediction of the long-term impact of a scientific article is challenging task, addressed by the bibliometrician through resorting to a proxy whose reliability increases with the breadth of the citation window. In the national research assessment exercises using metrics the citation window is necessarily short, but in some cases is sufficient to advise the use of simple citations. For the Italian VQR 2011---2014, the choice was instead made to adopt a linear weighted combination of citations and journal metric percentiles, with weights differentiated by discipline and year. Given the strategic importance of the exercise, whose results inform the allocation of a significant share of resources for the national academic system, we examined whether the predictive power of the proposed indicator is stronger than the simple citation count. The results show the opposite, for all discipline in the sciences and a citation window above 2 years.

Includes members:
  Member "do they agree bibliometric evaluation versus informed peer review in the italian research assessment exercise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  Member "the vqr italy s second national research assessment methodological failures and ranking distortions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when research assessment exercises leave room for opportunistic behavior by the subjects under evaluation")
  Member "inefficiency in selecting products for submission to national research assessment exercises":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when research assessment exercises leave room for opportunistic behavior by the subjects under evaluation")
  Member "national peer review research assessment exercises for the hard sciences can be a complete waste of money the italian case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  Member "the dangers of performance based research funding in non competitive higher education systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the impact of unproductive and top researchers on overall university research performance")
  Member "national research assessment exercises the effects of changing the rules of the game during the game":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best in class and strategic benchmarking of scientific subject categories of web of science in 2010")
  Member "citations versus journal impact factor as proxy of quality could the latter ever be preferable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "comparing the impact of subfields in scientific journals")
  Member "allocative efficiency in public research funding can bibliometrics help":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diversification versus specialization in scientific research which strategy pays off")

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)