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: "predicting results of the research excellence framework using departmental h index"
  Abstract: We compare estimates for past institutional research performances coming from two bibliometric indicators to the results of the UK's Research Assessment Exercise which last took place in 2008. We demonstrate that a version of the departmental h-index is better correlated with the actual results of that peer-review exercise than a competing metric known as the normalised citation-based indicator. We then determine the corresponding h-indices for 2008---2013, the period examined in the UK's Research Excellence Framework (REF) 2014. We place herewith the resulting predictions on the arXiv in advance of the REF results being published (December 2014). These may be considered as unbiased predictions of relative performances in that exercise. We will revisit this paper after the REF results are available and comment on the reliability or otherwise of these bibliometrics as compared with peer review.

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"
    - "peer review and citation data in predicting university rankings a large scale analysis"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "systematic analysis of agreement between metrics and peer review in the uk ref"):
  with members:
    - "peer review and citation data in predicting university rankings a large scale analysis"
    - "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 "peer review and citation data in predicting university rankings a large scale analysis"):
  with members:
    - "the coverage of microsoft academic analyzing the publication output of a university"
    - "do they agree bibliometric evaluation versus informed peer review in the italian research assessment exercise"

Connected through hyperedge HE-4 (induced by source paper "do they agree bibliometric evaluation versus informed peer review in the italian research assessment exercise"):
  with members:
    - "the vqr italy s second national research assessment methodological failures and ranking distortions"
    - "allocative efficiency in public research funding can bibliometrics help"

Connected through hyperedge HE-5 (induced by source paper "predicting results of the research excellence framework using departmental h index revisited"):
  with members:
    - "comparison of a citation based indicator and peer review for absolute and specific measures of research group excellence"

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)