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 "tie strength in online social networks and its applications a brief study"):
  Abstract: In online social network (OSN), understanding the factors bound to the role and strength of interaction(tie) are essential to model a wide variety of network-based applications. The recognition of these interactions can enhance the accuracy of link prediction, improve in ranking of dominants, reliability of recommendation and enhance targeted marketing as decision support system. In recent years, research interest on tie strength measures in OSN and its applications to diverse areas have increased, therefore it needs a comprehensive review covering tie strength estimation systematically. The objective of this paper is to provide an in-depth review, analyze and explore the tie strength in online social networks. A methodical category for tie strength estimation techniques are discussed and analyzed in a wide variety of network types. Representative applications of tie strength estimation are also addressed. Finally, a set of future challenges of the tie strength in online social networks is discussed.

Includes members:
  Member "detecting strong ties using network motifs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the power of pivoting for exact clique counting")
  Member "link classification and tie strength ranking in online social networks with exogenous interaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on using network science in mining developers collaboration in software engineering a systematic literature review")
  Member "routing in mobile ad hoc networks using social tie strengths and mobility plans":
    also connected through other hyperedges:
      (none)
  Member "link prediction in social networks the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")

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)