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: "group based trajectory modeling of citations in scholarly literature dynamic qualities of transient and sticky knowledge claims"
  Abstract: Group-based Trajectory Modeling (GBTM) is applied to the citation curves of articles in six journals and to all citable items in a single field of science (Virology, 24 journals), in order to distinguish among the developmental trajectories in subpopulations. Can highly-cited citation patterns be distinguished in an early phase as "fast-breaking" papers? Can "late bloomers" or "sleeping beauties" be identified? Most interesting, we find differences between "sticky knowledge claims" that continue to be cited more than ten years after publication, and "transient knowledge claims" that show a decay pattern after reaching a peak within a few years. Only papers following the trajectory of a "sticky knowledge claim" can be expected to have a sustained impact. These findings raise questions about indicators of "excellence" that use aggregated citation rates after two or three years (e.g., impact factors). Because aggregated citation curves can also be composites of the two patterns, 5th-order polynomials (with four bending points) are needed to capture citation curves precisely. For the journals under study, the most frequently cited groups were furthermore much smaller than ten percent. Although GBTM has proved a useful method for investigating differences among citation trajectories, the methodology does not enable us to define a percentage of highly-cited papers inductively across different fields and journals. Using multinomial logistic regression, we conclude that predictor variables such as journal names, number of authors, etc., do not affect the stickiness of knowledge claims in terms of citations, but only the levels of aggregated citations (that are field-specific).

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)