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 "metrics to evaluate research performance in academic institutions a critique of era 2010 as applied in forestry and the indirect h2 index as a possible alternative"):
  Abstract: Excellence for Research in Australia (ERA) is an attempt by the Australian Research Council to rate Australian universities on a 5-point scale within 180 Fields of Research using metrics and peer evaluation by an evaluation committee. Some of the bibliometric data contributing to this ranking suffer statistical issues associated with skewed distributions. Other data are standardised year-by-year, placing undue emphasis on the most recent publications which may not yet have reliable citation patterns. The bibliometric data offered to the evaluation committees is extensive, but lacks effective syntheses such as the h-index and its variants. The indirect H2 index is objective, can be computed automatically and efficiently, is resistant to manipulation, and a good indicator of impact to assist the ERA evaluation committees and to similar evaluations internationally.

Includes members:
  Member "evaluating research from informed peer review to bibliometrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when research assessment exercises leave room for opportunistic behavior by the subjects under evaluation")
  Member "rescaling citations of publications in physics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "should citations be field normalized in evaluative bibliometrics an empirical analysis based on propensity score matching")
  Member "an evaluation of the australian research council s journal ranking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessing evaluation procedures for individual researchers the case of the italian national scientific qualification")
  Member "allocative efficiency in public research funding can bibliometrics help":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diversification versus specialization in scientific research which strategy pays off")
  Member "ranking forestry journals using the h index":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "assessing the accuracy of the h and g indexes for measuring researchers productivity")

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)