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: "quantifying the influence of scientists and their publications distinguishing between prestige and popularity"
  Abstract: The number of citations is a widely used metric for evaluating the scientific credit of papers, scientists and journals. However, it so happens that papers with fewer citations from prestigious scientists have a higher influence than papers with more citations. In this paper, we argue that by whom the paper is being cited is of greater significance than merely the number of citations. Accordingly, we propose an interactive model of author–paper bipartite networks as well as an iterative algorithm to obtain better rankings for scientists and their publications. The main advantage of this method is twofold: (i) it is a parameter-free algorithm; (ii) it considers the relationship between the prestige of scientists and the quality of their publications. We conducted real experiments on publications in econophysics, and used this method to evaluate the influence of related scientific journals. The comparison between the rankings by our method and simple citation counts suggests that our method is effective in distinguishing prestige from popularity.

Connected through hyperedge HE-1 (induced by source paper "unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "early identification of important patents design and validation of citation network metrics"
    - "bias amplification in artificial intelligence systems"
    - "a review of theory and practice in scientometrics"
    - "a review of the literature on citation impact indicators"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "coauthorship and citation patterns in the physical review"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"

Connected through hyperedge HE-2 (induced by source paper "quantitative and qualitative analysis of editor behavior through maybe coercive citations"):
  with members:
    - "assessing the true role of coauthors in the h index measure of an author scientific impact"
    - "effects of publications in proceedings on the measure of the core size of coauthors"

Connected through hyperedge HE-3 (induced by source paper "network driven reputation in online scientific communities"):
  with members:
    - "quantifying long term scientific impact"
    - "pagerank standing on the shoulders of giants"

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)