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 "what do your friends think efficient polling methods for networks using friendship paradox"):
  Abstract: This paper deals with randomized polling of a social network. In the case of forecasting the outcome of an election between two candidates A and B, classical intent polling asks randomly sampled individuals: who will you vote for? Expectation polling asks: who do you think will win? In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A? Therefore, in NEP, sampled individuals will naturally look at their neighbors (defined by the underlying social network graph) when answering this question. Hence, the mean squared error (MSE) of NEP methods rely on selecting the optimal set of samples from the network. To this end, we propose three NEP algorithms for the following cases: (i) the social network graph is not known but, random walks (sequential exploration) can be performed on the graph (ii) the social network graph is unknown. For case (i) and (ii), two algorithms based on a graph theoretic consequence called friendship paradox are proposed. Theoretical results on the dependence of the MSE of the algorithms on the properties of the network are established. Numerical results on real and synthetic data sets are provided to illustrate the performance of the algorithms.

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 "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling")
  Member "friendship paradox biases perceptions in directed networks":
    also connected through other hyperedges:
      (none)
  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 "gemsec graph embedding with self clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  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 "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling")
  Member "online reputation and polling systems data incest social learning and revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance")
  Member "generalized friendship paradox in complex networks the case of scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "friendship paradox biases perceptions in directed 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)