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: "benchmarking some portuguese s t system research units"
  Abstract: The increasing use of productivity and impact metrics for evaluation and comparison, not only of individual researchers but also of institutions, universities and even countries, has prompted the development of bibliometrics. Currently, metrics are becoming widely accepted as an easy and balanced way to assist the peer review and evaluation of scientists and/or research units, provided they have adequate precision and recall. #R##N#This paper presents a benchmarking study of a selected list of representative Portuguese research units, based on a fairly complete set of parameters: bibliometric parameters, number of competitive projects and number of PhDs produced. The study aimed at collecting productivity and impact data from the selected research units in comparable conditions i.e., using objective metrics based on public information, retrievable on-line and/or from official sources and thus verifiable and repeatable. The study has thus focused on the activity of the 2003-06 period, where such data was available from the latest official evaluation. #R##N#The main advantage of our study was the application of automatic tools, achieving relevant results at a reduced cost. Moreover, the results over the selected units suggest that this kind of analyses will be very useful to benchmark scientific productivity and impact, and assist peer review.

Connected through hyperedge HE-1 (induced by source paper "benchmarking some portuguese s t system research units 2nd edition"):
  with members:
    - "a new era in citation and bibliometric analyses web of science scopus and google scholar"
    - "scientific impact quantity and quality analysis of two sources of bibliographic 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)