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: "a new approach to measure the scientific strengths of territories"
  Abstract: The current work applies a method for mapping the supply of new knowledge from public research organizations, in this case from Italian institutions at the level of regions and provinces (NUTS2 and NUTS3). Through the analysis of scientific production indexed in the Web of Science for the years 2006–2010, the new knowledge is classified in subject categories and mapped according to an algorithm for the reconciliation of authors' affiliations. Unlike other studies in the literature based on simple counting of publications, the present study adopts an indicator, Scientific Strength, which takes account of both the quantity of scientific production and its impact on the advancement of knowledge. The differences in the results that arise from the 2 approaches are examined. The results of works of this kind can inform public research policies, at national and local levels, as well as the localization strategies of research-based companies.

Connected through hyperedge HE-1 (induced by source paper "a methodology to compute the territorial productivity of scientists the case of italy"):
  with members:
    - "mapping excellence in national research systems the case of italy"
    - "measuring science irresistible temptations easy shortcuts and dangerous consequences"
    - "a new bibliometric approach to assess the scientific specialization of regions"
    - "how do you define and measure research productivity"
    - "individual research performance a proposal for comparing apples to oranges"
    - "revisiting size effects in higher education research productivity"
    - "revisiting the scaling of citations for research assessment"
    - "mapping excellence in the geography of science an approach based on scopus data"

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)