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: "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"
  Abstract: The findings of Bornmann, Leydesdorff, and Wang (in press) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years.

Connected through hyperedge HE-1 (induced by source paper "predicting citation counts based on deep neural network learning techniques"):
  with members:
    - "can scientific impact be predicted"
    - "deep residual learning for image recognition"
    - "a review of the characteristics of 108 author level bibliometric indicators"
    - "very deep convolutional networks for large scale image recognition"
    - "bibliometric indicators of young authors in astrophysics can later stars be predicted"
    - "neural machine translation by jointly learning to align and translate"
    - "will this paper increase your h index scientific impact prediction"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-2 (induced by source paper "predicting publication long term impact through a combination of early citations and journal impact factor"):
  with members:
    - "revisiting the scientometric conceptualization of impact and its measurement"
    - "refrain from adopting the combination of citation and journal metrics to grade publications as used in the italian national research assessment exercise vqr 2011 2014"
    - "predicting the long term citation impact of recent publications"
    - "quantifying long term scientific impact"
    - "revisiting the scaling of citations for research assessment"
    - "assessing the varying level of impact measurement accuracy as a function of the citation window length"
    - "citations versus journal impact factor as proxy of quality could the latter ever be preferable"

Connected through hyperedge HE-3 (induced by source paper "predicting publication productivity for researchers a latent variable model"):
  with members:
    - "predicting citation counts based on deep neural network learning techniques"
    - "predicting publication productivity for researchers a piecewise poisson model"
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "can scientific impact be predicted"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-4 (induced by source paper "the citation advantage of foreign language references for chinese social science papers"):
  with members:
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "regression for citation data an evaluation of different methods"
    - "comparing journals from different fields of science and social science through a jcr subject categories normalized impact factor"
    - "self selected or mandated open access increases citation impact for higher quality research"

Connected through hyperedge HE-5 (induced by source paper "predicting publication productivity for researchers a piecewise poisson model"):
  with members:
    - "predicting citation counts based on deep neural network learning techniques"
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "can scientific impact be predicted"
    - "accuracy of simple initials based methods for author name disambiguation"

Connected through hyperedge HE-6 (induced by source paper "revisiting the scientometric conceptualization of impact and its measurement"):
  with members:
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "a farewell to the mncs and like size independent indicators"
    - "refrain from adopting the combination of citation and journal metrics to grade publications as used in the italian national research assessment exercise vqr 2011 2014"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "citations indicators of quality the impact fallacy"
    - "a review of theory and practice in scientometrics"
    - "predicting the long term citation impact of recent publications"

Connected through hyperedge HE-7 (induced by source paper "on the relationships between bibliographic characteristics of scientific documents and citation and mendeley readership counts a large scale analysis of web of science publications"):
  with members:
    - "on the differences between citations and altmetrics an investigation of factors driving altmetrics vs citations for finnish articles"
    - "what makes papers visible on social media an analysis of various document characteristics"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "interpreting altmetrics viewing acts on social media through the lens of citation and social theories"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "regression for citation data an evaluation of different methods"

Connected through hyperedge HE-8 (induced by source paper "nncp a citation count prediction methodology based on deep neural network learning techniques"):
  with members:
    - "deep residual learning for image recognition"
    - "a review of the characteristics of 108 author level bibliometric indicators"
    - "very deep convolutional networks for large scale image recognition"
    - "bibliometric indicators of young authors in astrophysics can later stars be predicted"
    - "deep visual semantic alignments for generating image descriptions"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

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)