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 "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling"):
  Abstract: This paper considers the problem of estimating a power-law degree distribution of an undirected network. Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them~(e.g.~linear regression on double-logarithmic axes, maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution. As a solution, we present a novel maximum likelihood estimation approach that exploits the \textit{friendship paradox} to sample more efficiently from the tail of the degree distribution. We analytically show that the proposed method results in a smaller bias, variance and a Cram\`{e}r-Rao lower bound compared to the maximum-likelihood estimate obtained with uniformly sampled nodes~(which is the commonly used method in literature). Detailed simulation results are presented to illustrate the performance of the proposed method under different conditions and how it compares with alternative methods.

Includes members:
  Member "information diffusion in social networks friendship paradox based models and statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox")
  Member "centrality friendship paradoxes when our friends are more important than us":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "which friends are more popular than you contact strength and the friendship paradox in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "the majority illusion in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "qualities and inequalities in online social networks through the lens of the generalized friendship paradox":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox")
  Member "tracking infection diffusion in social networks filtering algorithms and threshold bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "network weirdness exploring the origins of network paradoxes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "friendship paradox biases perceptions in directed networks")
  Member "friendship paradox redux your friends are more interesting than you":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")

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)