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: "should citations be field normalized in evaluative bibliometrics an empirical analysis based on propensity score matching"
  Abstract: Field normalization of citations is bibliometric standard. Despite the observed impact differences between fields, the question remains how strong fields influence the citation impact of papers. We wondered whether field differences can be traced back to other factors (than fields) possibly influencing citations (FICs) or are "pure" field effects. We considered several FICs such as number of pages and number of co-authors. Using the propensity score matching approach, we investigated with Web of Science data whether papers with the same or similar attributes (e.g., number of co-authors and number of pages) received different citations when they were assigned to different fields. In a diagnostic step of our statistical analyses, we considered propensity scores as covariates in regression analyses to examine whether the differences between the fields vanish. The results revealed that the differences did not completely vanish, but were significantly reduced. We received similar results when we calculated mean value differences of the fields after inverse-probability of treatment weighting representing the causal or unconfounded field effects on citations. One possible interpretation of our results is that field-differences in citations exist which are relatively independent of other FICs. Another possible interpretation is that field-differences could vanish in principle.

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)