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 "can scientific impact be predicted"):
  Abstract: A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my  h -index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors’ future  h -indices based on their current scientific impact. Second, we examine the factors that drive papers—either previously or newly published—to increase their authors’ predicted future  h -indices. By leveraging relevant factors, we can predict an author's  h -index in five years with an    $R^2$       value of 0.92 and whether a previously (newly) published paper will contribute to this future  h -index with an   $F_1$       score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed  h  -index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants.”

Includes members:
  Member "modeling and predicting popularity dynamics via reinforced poisson processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepcp deep learning driven cascade prediction based autonomous content placement in closed social network")
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "will this paper increase your h index scientific impact prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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")
  Member "citing for high impact":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "co citations in context disciplinary heterogeneity is relevant")
  Member "information diffusion in computer science citation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coloring large complex networks")

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)