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 "predicting publication productivity for researchers a latent variable model"):
  Abstract: This study provided a model for the publication dynamics of researchers, which is based on the relationship between the publication productivity of researchers and two covariates: time and historical publication quantity. The relationship allows to estimate the latent variable the publication creativity of researchers. The variable is applied to the prediction of publication productivity for researchers. The statistical significance of the relationship is validated by the high quality dblp dataset. The effectiveness of the model is testified on the dataset by the fine fittings on the quantitative distribution of researchers' publications, the evolutionary trend of their publication productivity, and the occurrence of publication events. Due to its nature of regression, the model has the potential to be extended for assessing the confidence level of prediction results, and thus has clear applicability to empirical research.

Includes members:
  Member "predicting citation counts based on deep neural network learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting publication productivity for researchers a piecewise poisson model")
  Member "predicting publication productivity for researchers a piecewise poisson model":
    also connected through other hyperedges:
      (none)
  Member "predicting publication long term impact through a combination of early citations and journal impact factor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  Member "can scientific impact be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting citation counts based on deep neural network learning techniques")
  Member "how to improve the prediction based on citation impact percentiles for years shortly after the publication date":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting citation counts based on deep neural network learning techniques")
  Member "quantifying long term scientific impact":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators")

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)