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: "mining university rankings publication output and citation impact as their basis"
  Abstract: World University rankings have become well-established tools that students, university managers and policy makers read and use. Each ranking claims to have a unique methodology capable of measuring the 'quality' of universities. The purpose of this paper is to analyze to which extent these different rankings measure the same phenomenon and what it is that they are measuring. For this, we selected a total of seven world-university rankings and performed a principal component analysis. After ensuring that despite their methodological differences, they all come together to a single component, we hypothesized that bibliometric indicators could explain what is being measured. Our analyses show that ranking scores from whichever of the seven league tables under study can be explained by the number of publications and citations received by the institution. We conclude by discussing policy implications and opportunities on how a nuanced and responsible use of rankings can help decision making at the institutional level

Connected through hyperedge HE-1 (induced by source paper "the differing meanings of indicators under different policy contexts the case of internationalisation"):
  with members:
    - "making sense of global collaboration dynamics developing a methodological framework to study dis similarities between country disciplinary profiles and choice of collaboration partners"
    - "the many faces of mobility using bibliometric data to measure the movement of scientists"
    - "do interdisciplinary research teams deliver higher gains to science"
    - "global research collaboration networks and partners in south east asia"
    - "a critical comparative analysis of five world university rankings"
    - "interpreting altmetrics viewing acts on social media through the lens of citation and social theories"
    - "towards a global scientific brain indicators of researcher mobility using co affiliation data"
    - "how journal rankings can suppress interdisciplinary research a comparison between innovation studies and business management"

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)