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: "information diffusion in social networks friendship paradox based models and statistical inference"
  Abstract: Dynamic models and statistical inference for the diffusion of information in social networks is an area which has witnessed remarkable progress in the last decade due to the proliferation of social networks. Modeling and inference of diffusion of information has applications in targeted advertising and marketing, forecasting elections, predicting investor sentiment and identifying epidemic outbreaks. This chapter discusses three important aspects related to information diffusion in social networks: (i) How does observation bias due to the friendship paradox (on average your friends have more friends than you do) and monophilic contagion (influence of friends of friends) affect the information diffusion dynamics? (ii) How can social networks adapt their structural connectivity depending on the state of information diffusion? (iii) How one can estimate the state of the network induced by information diffusion? The motivation for these three topics stems from recent results in network science and social sensing.

Connected through hyperedge HE-1 (induced by source paper "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling"):
  with members:
    - "centrality friendship paradoxes when our friends are more important than us"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "the majority illusion in social networks"
    - "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
    - "tracking infection diffusion in social networks filtering algorithms and threshold bounds"
    - "network weirdness exploring the origins of network paradoxes"
    - "friendship paradox redux your friends are more interesting than you"
    - "using friends as sensors to detect global scale contagious outbreaks"

Connected through hyperedge HE-2 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox"):
  with members:
    - "friendship paradox biases perceptions in directed networks"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "gemsec graph embedding with self clustering"
    - "the majority illusion in social networks"
    - "qualities and inequalities in online social networks through the lens of the generalized friendship paradox"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"

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)