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 "identification of influencers in complex networks by local information dimension"):
  Abstract: Identifying influential spreaders in the complex network has been a hot topic in the study of network characteristic. Lots of centrality measures have been proposed to address this problem, but most methods have their own limitations and shortcomings. In this paper, a novel method is proposed to identify influencers in complex networks via the local information dimension. This proposed method considers the local structure property around the central node, so the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus this proposed method considers the quasi-local information and reduces the computational complexity. The information (number of nodes) in boxes is considered by Shannon entropy, which is more reasonable. The node would be more influential when the local information dimension of this node is higher. In order to show the effectiveness of this proposed method, five exiting centrality measures are used as comparison methods to rank influential nodes in six real-world complex networks. In addition, Susceptible-Infected (SI) model and Kendall's tau coefficient are applied to show the correlation between different methods. The experiment results show the superiority of this proposed method.

Includes members:
  Member "identifying influential nodes based on fuzzy local dimension in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the vulnerability of communities in complex network an entropy approach")
  Member "an evidential markov decision making model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying influential nodes based on fuzzy local dimension in complex networks")
  Member "improving detection of influential nodes in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "agent based opinion formation modeling in social network a perspective of social psychology")

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)