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: "the scaling relationship between citation based performance and coauthorship patterns in natural sciences"
  Abstract: The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and collaboration patterns for papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through collaboration account for 89%. The citation-based performance and collaboration patterns exhibit a power-law correlation with a scaling exponent of 1.20 ± 0.07. Citations to a subfield’s research articles tended to increase 2.1.20 or 2.30 times each time it doubles the number of collaborative papers. The scaling exponent for the power-law relationship for single-authored papers was 0.85 ± 0.11. The citations to a subfield’s single-authored research articles increased 2.0.85 or 1.89 times each time the research area doubles the number of non-collaborative papers. The Matthew effect is stronger for collaborated papers than for single-authored. In fact, with a scaling exponent < 1.0 the impact of single-author papers exhibits a cumulative disadvantage or inverse Matthew effect.

Connected through hyperedge HE-1 (induced by source paper "the evolutions of the rich get richer and the fit get richer phenomena in scholarly networks the case of the strategic management journal"):
  with members:
    - "quantifying long term scientific impact"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"
    - "cwts crown indicator measures citation impact of a research group s publication oeuvre"
    - "towards a new crown indicator an empirical analysis"
    - "experience versus talent shapes the structure of the web"

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)