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: "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.

Connected through hyperedge HE-1 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference"):
  with members:
    - "centrality friendship paradoxes when our friends are more important than us"
    - "conjoining uncooperative societies facilitates evolution of cooperation"
    - "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "engagement dynamics and sensitivity analysis of youtube videos"
    - "the majority illusion in social networks"
    - "the happiness paradox your friends are happier than you"

Connected through hyperedge HE-2 (induced by source paper "friendship paradox biases perceptions in directed networks"):
  with members:
    - "do diffusion protocols govern cascade growth"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "the majority illusion in social networks"
    - "the h index paradox your coauthors have a higher h index than you do"
    - "network weirdness exploring the origins of network paradoxes"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "clustering and community detection in directed networks a survey"
    - "friendship paradox redux your friends are more interesting than you"

Connected through hyperedge HE-3 (induced by source paper "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"):
  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"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "the happiness paradox your friends are happier than you"
    - "tracking infection diffusion in social networks filtering algorithms and threshold bounds"
    - "scalable methods for adaptively seeding a social network"
    - "friendship paradox redux your friends are more interesting than you"
    - "using friends as sensors to detect global scale contagious outbreaks"

Connected through hyperedge HE-4 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance"):
  with members:
    - "coarse graining and the blackwell order"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "active sequential hypothesis testing"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-5 (induced by source paper "influence maximization over markovian graphs a stochastic optimization approach"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "exact recovery in the stochastic block model"
    - "interactive sensing and decision making in social networks"
    - "influence maximization in continuous time diffusion networks"
    - "uncovering the temporal dynamics of diffusion networks"
    - "information spreading in stationary markovian evolving graphs"

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)