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: "peer review versus bibliometrics which method better predicts the scholarly impact of publications"
  Abstract: In this work, we try to answer the question of which method, peer review versus bibliometrics, better predicts the future overall scholarly impact of scientific publications. We measure the agreement between peer review evaluations of Web of Science indexed publications submitted to the first Italian research assessment exercise and long-term citations of the same publications. We do the same for an early citation-based indicator. We find that the latter shows stronger predictive power, i.e. it more reliably predicts late citations in all the disciplinary areas examined, and for any citation time window starting 1 year after publication.

Connected through hyperedge HE-1 (induced by source paper "when research assessment exercises leave room for opportunistic behavior by the subjects under evaluation"):
  with members:
    - "diversification versus specialization in scientific research which strategy pays off"
    - "on tit for tat franceschini and maisano versus anvur regarding the italian research assessment exercise vqr 2011 2014"
    - "bibliometric evaluation of research performance where do we stand"
    - "do they agree bibliometric evaluation versus informed peer review in the italian research assessment exercise"
    - "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"
    - "the vqr italy s second national research assessment methodological failures and ranking distortions"
    - "the determinants of academic career advancement evidence from italy"
    - "inefficiency in selecting products for submission to national research assessment exercises"

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)