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: "research assessment by percentile based double rank analysis"
  Abstract: In the double rank analysis of research publications, the local rank position of a country or institution publication is expressed as a function of the world rank position. Excluding some highly or lowly cited publications, the double rank plot fits well with a power law, which can be explained because citations for local and world publications follow lognormal distributions. We report here that the distribution of the number of country or institution publications in world percentiles is a double rank distribution that can be fitted to a power law. Only the data points in high percentiles deviate from it when the local and world μ parameters of the lognormal distributions are very different. The likelihood of publishing very highly cited papers can be calculated from the power law that can be fitted either to the upper tail of the citation distribution or to the percentile-based double rank distribution. The great advantage of the latter method is that it has universal application, because it is based on all publications and not just on highly cited publications. Furthermore, this method extends the application of the well-established percentile approach to very low percentiles where breakthroughs are reported but paper counts cannot be performed.

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:
    - "like for like bibliometric substitutes for peer review advantages and limits of indicators calculated from the ep index"
    - "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 "technological research in the eu is less efficient than the world average eu research policy risks europeans future"):
  with members:
    - "the elephant in the room the problem of quantifying productivity in evaluative scientometrics"
    - "brics countries and scientific excellence a bibliometric analysis of most frequently cited papers"
    - "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"
    - "on the calculation of percentile based bibliometric indicators"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "the leiden ranking 2011 2012 data collection indicators and interpretation"
    - "the knowledge based economy and the triple helix model"

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)