human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "nepotistic relationships in twitter and their impact on rank prestige algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling user reputation in online social networks the role of costs benefits and reciprocity")
  Member "influence and passivity in social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership")
  Member "big macs and eigenfactor scores don t let correlation coefficients fool you":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "systematic analysis of agreement between metrics and peer review in the uk ref")
  Member "social networks that matter twitter under the microscope":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consistent recovery threshold of hidden nearest neighbor graphs")

Question: Please tell me which class the source paper 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)