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: "assessing the true role of coauthors in the h index measure of an author scientific impact"
  Abstract: A method based on the classical principal component analysis leads to demonstrate that the role of co-authors should give a h-index measure to a group leader higher than usually accepted. The method rather easily gives what is usually searched for, i.e. an estimate of the role (or “weight”) of co-authors, as the additional value to an author papers’ popularity. The construction of the co-authorship popularity H-matrix is exemplified and the role of eigenvalues and the main eigenvector component are discussed. Examples illustrate the points and serve as the basis for suggesting a generally practical application of the concept.

Connected through hyperedge HE-1 (induced by source paper "h α an index to quantify an individual s scientific leadership"):
  with members:
    - "a modification to hirsch index allowing comparisons across different scientific fields"
    - "central indexes to the citation distribution a complement to the h index"
    - "the inconsistency of the h index"

Connected through hyperedge HE-2 (induced by source paper "quantitative and qualitative analysis of editor behavior through maybe coercive citations"):
  with members:
    - "effects of publications in proceedings on the measure of the core size of coauthors"
    - "quantifying the influence of scientists and their publications distinguishing between prestige and popularity"

Connected through hyperedge HE-3 (induced by source paper "bibliometric author evaluation through linear regression on the coauthor network"):
  with members:
    - "field normalized citation impact indicators and the choice of an appropriate counting method"
    - "a review of the literature on citation impact indicators"

Connected through hyperedge HE-4 (induced by source paper "analyzing research performance proposition of a new complementary index"):
  with members:
    - "citex a new citation index to measure the relative importance of authors and papers in scientific publications"
    - "the bibliometric properties of article readership information"

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)