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: "conceptual quantification of the dynamicity of longitudinal social networks"
  Abstract: A longitudinal social network evolves over time through the creation and/or deletion of links among a set of actors (e.g. individuals or organisations). Longitudinal social networks are studied by network science and social science researchers to understand network evolution, trend propagation, friendship and belief formation, diffusion of innovations, the spread of deviant behaviour and more. In the current literature, there are different approaches and methods (e.g. Sampson's approach and the Markov model) to study the dynamics of longitudinal social networks. These approaches and methods have mainly been utilised to explore evolutionary changes of longitudinal social networks from one state to another and to explain the underlying reasons for these changes. However, they cannot quantify the level of dynamicity of the over time network changes and the contribution of individual network members (i.e. actors) to these changes. In this study, we first develop a set of measures to quantify different aspects of the dynamicity of a longitudinal social network. We then apply these measures, in order to conduct empirical investigations, to two different longitudinal social networks. Finally, we discuss the implications of the application of these measures and possible future research directions of this study.

Connected through hyperedge HE-1 (induced by source paper "a framework to explore the knowledge structure of multidisciplinary research fields"):
  with members:
    - "entitymetrics measuring the impact of entities"
    - "evolutionary dynamics of scientific collaboration networks multi levels and cross time analysis"

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.SI(Social and Information Networks)