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 "friendship paradox biases perceptions in directed networks"):
  Abstract: How popular a topic or an opinion appears to be in a network can be very different from its actual popularity. For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i.e., its global popularity---can be dramatically different from how people see it in their social feeds---i.e., its perceived popularity---where the feeds aggregate their friends' posts. We trace the origin of this discrepancy to the friendship paradox in directed networks, which states that people are less popular than their friends (or followers) are, on average. We identify conditions on network structure that give rise to this perception bias, and validate the findings empirically using data from Twitter. Within messages posted by Twitter users in our sample, we identify topics that appear more frequently within the users' social feeds, than they do globally, i.e., among all posts. In addition, we present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic's global prevalence from biased perceptions of individuals. We characterize the bias of the polling estimate, provide an upper bound for its variance, and validate the algorithm's efficiency through synthetic polling experiments on our Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort social perceptions and resulting behaviors.

Includes members:
  Member "what do your friends think efficient polling methods for networks using friendship paradox":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference")
  Member "do diffusion protocols govern cascade growth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatically applying a credibility appraisal tool to track vaccination related communications shared on social media")
  Member "centrality friendship paradoxes when our friends are more important than us":
    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 "the h index paradox your coauthors have a higher h index than you do":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "building the brazilian academic genealogy tree")
  Member "network weirdness exploring the origins of network paradoxes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "maximum likelihood estimation of power law degree distributions using friendship paradox based sampling")
  Member "generalized friendship paradox in complex networks the case of scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what do your friends think efficient polling methods for networks using friendship paradox")
  Member "clustering and community detection in directed networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of community search over big graphs")

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)