human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading"):
  with members:
    - "research assessment by percentile based double rank analysis"
    - "not dead just resting the practical value of per publication citation indicators"
    - "how do you define and measure research productivity"
    - "the inconsistency of the h index"

Connected through hyperedge HE-2 (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"):
  with members:
    - "systematic analysis of agreement between metrics and peer review in the uk ref"
    - "technological research in the eu is less efficient than the world average eu research policy risks europeans future"
    - "how do you define and measure research productivity"
    - "the european union china and the united states in the top 1 and top 10 layers of most frequently cited publications competition and collaborations"

Question: Please tell me which class the center node 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)