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 piecewise poisson model"):
  Abstract: Predicting the scientific productivity of researchers is a basic task for academic administrators and funding agencies. This study provided a model for the publication dynamics of researchers, inspired by the distribution feature of researchers' publications in quantity. It is a piecewise Poisson model, analyzing and predicting the publication productivity of researchers by regression. The principle of the model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The model's principle was validated by the high quality dblp dataset, and its effectiveness was testified in predicting the publication productivity for majority of researchers and the evolutionary trend of their publication productivity. Tests to confirm or disconfirm the model are also proposed. The model has the advantage of providing results in an unbiased way; thus is useful for funding agencies that evaluate a vast number of applications with a quantitative index on publications.

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 latent variable model")
  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 "accuracy of simple initials based methods for author name disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "topic modeling the reading and writing behavior of information foragers")
  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-5 (induced by source paper "predicting citation counts based on deep neural network learning techniques")

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)