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: "a variant of the h index to measure recent performance"
  Abstract: The predictive power of the h-index has been shown to depend on citations to rather old publications. This has raised doubts about its usefulness for predicting future scientific achievements. Here, I investigate a variant that considers only recent publications and is therefore more useful in academic hiring processes and for the allocation of research resources. It is simply defined in analogy to the usual h-index, but takes into account only publications from recent years, and it can easily be determined from the ISI Web of Knowledge.

Connected through hyperedge HE-1 (induced by source paper "h index and its alternative a review"):
  with members:
    - "restricting the h index to a publication and citation time window a case study of a timed hirsch index"
    - "a review of the characteristics of 108 author level bibliometric indicators"
    - "impact vitality a measure for excellent scientists"
    - "socializing the h index"
    - "simple arithmetic versus intuitive understanding the case of the impact factor"
    - "central indexes to the citation distribution a complement to the h index"
    - "the inconsistency of the h index"
    - "weighted indices for evaluating the quality of research with multiple authorship"

Connected through hyperedge HE-2 (induced by source paper "restricting the h index to a publication and citation time window a case study of a timed hirsch index"):
  with members:
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"
    - "towards a new crown indicator an empirical analysis"

Connected through hyperedge HE-3 (induced by source paper "predicting results of the research excellence framework using departmental h index"):
  with members:
    - "comparison of a citation based indicator and peer review for absolute and specific measures of research group excellence"

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)