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: "quality related publication categories in social sciences and humanities based on a university s peer review assessments"
  Abstract: Bibliometric analysis has firmly conquered its place as an instrument for evaluation and international comparison of performance levels. Consequently, differences in coverage by standard bibliometric databases installed a dichotomy between on the one hand the well covered 'exact' sciences, and on the other hand most of the social sciences and humanities with a more limited coverage (Nederhof, 2006). Also the latter domains need to be able to soundly demonstrate their level of performance and claim or legitimate funding accordingly. An important part of the output volume in social sciences appears as books, book chapters and national literature (Hicks, 2004). To proceed from publication data to performance measurement, quantitative publication counts need to be combined with qualitative information, for example from peer assessment or validation (European Expert Group on Assessment of University-Based Research, 2010), to identify those categories that represent research quality as perceived by peers. An accurate focus is crucial in order to stimulate, recognize and reward high quality achievements only. This paper demonstrates how such a selection of publication categories can be based on correlations with peer judgments. It is also illustrated that the selection should be sufficiently precise, to avoid subcategories negatively correlated with peer judgments. The findings indicate that, also in social sciences and humanities, publications in journals with an international referee system are the most important category for evaluating quality. Book chapters with international referee system and contributions in international conference proceedings follow them.

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)