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 "the contribution of star scientists to overall sex differences in research productivity"):
  Abstract: The state of the art on the issue of sex differences in research efficiency agrees in recognizing higher performances for males, however there are divergences in explaining the possible causes. One of the causes advanced is that there are sex differences in the availability of aptitude at the “high end”. By comparing sex differences in concentration and performance of Italian academic star scientists to the case in the population complement, this work aims to verify if star, or “high-end”, scientists play a preponderant role in determining higher performance among males. The study reveals the existence of a greater relative concentration of males among star scientists, as well as a performance gap between male and female star scientists that is greater than for the rest of the population. In the latter subpopulation the performance gap between the two sexes is seen as truly marginal.

Includes members:
  Member "measuring science irresistible temptations easy shortcuts and dangerous consequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the collaboration behavior of top scientists")
  Member "gender differences in research productivity a bibliometric analysis of the italian academic system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the collaboration behavior of top scientists")
  Member "the measurement of italian universities research productivity by a non parametric bibliometric methodology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mapping excellence in national research systems the case of italy")

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)