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: "regions innovation systems and the north south divide in italy"
  Abstract: Using firm-level data collected by Statistics Italy for 2008, 2011, and 2015, we examine the Triple-Helix synergy among geographical and size distributions of firms, and the NACE codes attributed to these firms, at the different levels of regional and national government. At which levels is innovation-systemness indicated? The contributions of regions to the Italian innovation system have increased, but synergy generation between regions and supra-regionally has remained at almost 45%. As against the statistical classification of Italy into twenty regions or into Northern, Central, and Southern Italy, the greatest synergy is retrieved by considering the country in terms of Northern and Southern Italy as two sub-systems, with Tuscany included as part of Northern Italy. We suggest that separate innovation strategies should be developed for these two parts of the country. The current focus on regions for innovation policies may to some extent be an artifact of the statistics and EU policies. In terms of sectors, both medium- and high-tech manufacturing (MHTM) and knowledge-intensive services (KIS) are proportionally integrated in the various regions.

Connected through hyperedge HE-1 (induced by source paper "synergy in the knowledge base of u s innovation systems at national state and regional levels the contributions of high tech manufacturing and knowledge intensive services"):
  with members:
    - "a triple helix model of medical innovation supply demand and technological capabilities in terms of medical subject headings"
    - "measuring triple helix synergy in the russian innovation systems at regional provincial and national levels"
    - "measuring the knowledge based economy of china in terms of synergy among technological organizational and geographic attributes of firms"
    - "the swedish system of innovation regional synergies in a knowledge based economy"
    - "a routine for measuring synergy in university industry government relations mutual information as a triple helix and quadruple helix indicator"
    - "rotational symmetry and the transformation of innovation systems in a triple helix of university industry government relations"

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)