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: "the elephant in the room the problem of quantifying productivity in evaluative scientometrics"
  Abstract: In a critical and provocative paper, Abramo and D'Angelo claim that commonly used scientometric indicators such as the mean normalized citation score (MNCS) are completely inappropriate as indicators of scientific performance. Abramo and D'Angelo argue that scientific performance should be quantified using indicators that take into account the productivity of a research unit. We provide a response to Abramo and D'Angelo, indicating where we believe they raise important issues, but also pointing out where we believe their claims to be too extreme.

Connected through hyperedge HE-1 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities"):
  with members:
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "the impact of unproductive and top researchers on overall university research performance"
    - "the ratio of top scientists to the academic staff as an indicator of the competitive strength of universities"
    - "not dead just resting the practical value of per publication citation indicators"
    - "efficiency of research performance and the glass researcher"
    - "ranking research institutions by the number of highly cited articles per scientist"
    - "predicting the long term citation impact of recent publications"
    - "how do you define and measure research productivity"

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:
    - "research assessment by percentile based double rank analysis"
    - "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)