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 and citation data in predicting university rankings a large scale analysis"
  Abstract: Most Performance-based Research Funding Systems (PRFS) draw on peer review and bibliometric indicators, two different methodologies which are sometimes combined. A common argument against the use of indicators in such research evaluation exercises is their low correlation at the article level with peer review judgments. In this study, we analyse 191,000 papers from 154 higher education institutes which were peer reviewed in a national research evaluation exercise. We combine these data with 6.95 million citations to the original papers. We show that when citation-based indicators are applied at the institutional or departmental level, rather than at the level of individual papers, surprisingly large correlations with peer review judgments can be observed, up to r <= 0.802, n = 37, p < 0.001 for some disciplines. In our evaluation of ranking prediction performance based on citation data, we show we can reduce the mean rank prediction error by 25% compared to previous work. This suggests that citation-based indicators are sufficiently aligned with peer review results at the institutional level to be used to lessen the overall burden of peer review on national evaluation exercises leading to considerable cost savings.

Connected through hyperedge HE-1 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications"):
  with members:
    - "systematic analysis of agreement between metrics and peer review in the uk ref"
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "do citations and readership identify seminal publications"
    - "revisiting the scientometric conceptualization of impact and its measurement"
    - "selecting competent referees to assess research projects proposals a study of referees registers"
    - "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"
    - "citations indicators of quality the impact fallacy"

Connected through hyperedge HE-2 (induced by source paper "systematic analysis of agreement between metrics and peer review in the uk ref"):
  with members:
    - "predicting results of the research excellence framework using departmental h index"
    - "predicting results of the research excellence framework using departmental h index revisited"
    - "comparison of a citation based indicator and peer review for absolute and specific measures of research group excellence"
    - "a new methodology for constructing a publication level classification system of science"
    - "big macs and eigenfactor scores don t let correlation coefficients fool you"

Connected through hyperedge HE-3 (induced by source paper "heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation"):
  with members:
    - "systematic analysis of agreement between metrics and peer review in the uk ref"
    - "critical rationalism and the search for standard field normalized indicators in bibliometrics"
    - "can the journal impact factor be used as a criterion for the selection of junior researchers a large scale empirical study based on researcherid data"
    - "a review of the literature on citation impact indicators"
    - "how to evaluate individual researchers working in the natural and life sciences meaningfully a proposal of methods based on percentiles of citations"

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)