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 "measuring transport difficulty of data dissemination in large scale online social networks an interest driven case"):
  Abstract: In this paper, we aim to model the formation of data dissemination in online social networks (OSNs), and measure the transport difficulty of generated data traffic. We focus on a usual type of interest-driven social sessions in OSNs, called \emph{Social-InterestCast}, under which a user will autonomously determine whether to view the content from his followees depending on his interest. It is challenging to figure out the formation mechanism of such a Social-InterestCast, since it involves multiple interrelated factors such as users' social relationships, users' interests, and content semantics. We propose a four-layered system model, consisting of physical layer, social layer, content layer, and session layer. By this model we successfully obtain the geographical distribution of Social-InterestCast sessions, serving as the precondition for quantifying data transport difficulty. We define the fundamental limit of \emph{transport load} as a new metric, called \emph{transport complexity}, i.e., the \emph{minimum required} transport load for an OSN over a given carrier network. Specifically, we derive the transport complexity for Social-InterestCast sessions in a large-scale OSN over the carrier network with optimal communication architecture. The results can act as the common lower bounds on transport load for Social-InterestCast over any carrier networks. To the best of our knowledge, this is the first work to measure the transport difficulty for data dissemination in OSNs by modeling session patterns with the interest-driven characteristics.

Includes members:
  Member "campaigning in heterogeneous social networks optimal control of si information epidemics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks")
  Member "conjoining speeds up information diffusion in overlaying social physical networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges in the decentralised web the mastodon case")
  Member "the untold story of the clones content agnostic factors that impact youtube video popularity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying the sentiment styles of youtube s vloggers")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")

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)