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: "de retibus socialibus et legibus momenti"
  Abstract: Online Social Networks (OSNs) are a cutting edge topic. Almost everybody —users, marketers, brands, companies, and researchers— is approaching OSNs to better understand them and take advantage of their benefits. Maybe one of the key concepts underlying OSNs is that of influence which is highly related, although not entirely identical, to those of popularity and centrality. Influence is, according to Merriam-Webster, "the capacity of causing an effect in indirect or intangible ways". Hence, in the context of OSNs, it has been proposed to analyze the clicks received by promoted URLs in order to check for any positive correlation between the number of visits and different "influence" scores. That evaluation methodology is used in this letter to compare a number of those techniques with a new method firstly described here. That new method is a simple and rather elegant solution which tackles with influence in OSNs by applying a physical metaphor.

Connected through hyperedge HE-1 (induced by source paper "measuring user influence on twitter"):
  with members:
    - "a review of features for the discrimination of twitter users application to the prediction of offline influence"
    - "truetop a sybil resilient system for user influence measurement on twitter"
    - "klout score measuring influence across multiple social networks"
    - "detecting real world influence through twitter"
    - "influencetracker rating the impact of a twitter account"
    - "nepotistic relationships in twitter and their impact on rank prestige algorithms"
    - "influence and passivity in social media"

Connected through hyperedge HE-2 (induced by source paper "nepotistic relationships in twitter and their impact on rank prestige algorithms"):
  with members:
    - "influence and passivity in social media"
    - "birds of a feather does user homophily impact information diffusion in social media"
    - "social networks that matter twitter under the microscope"

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)