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 "measuring user influence on twitter"):
  Abstract: We survey the several measures that exists in literature to rank influential users in Twitter network.We propose a classification of these measures according to different criteria, such as the kind of metrics, the use of PageRank algorithm, the use of content analysis, among others.Besides the influential users, we also study measures of activity and popularity.We finish by mentioning some aspects of this topic related with computational complexity and correlation measures. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

Includes members:
  Member "a review of features for the discrimination of twitter users application to the prediction of offline influence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review of inferring latent attributes from twitter")
  Member "truetop a sybil resilient system for user influence measurement on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structure based sybil detection in social networks via local rule based propagation")
  Member "klout score measuring influence across multiple social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "midmod osn a microscopic level information diffusion model for online social networks")
  Member "detecting real world influence through twitter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a review of features for the discrimination of twitter users application to the prediction of offline influence")
  Member "influencetracker rating the impact of a twitter account":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting rising follower counts on twitter using profile information")
  Member "nepotistic relationships in twitter and their impact on rank prestige algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling user reputation in online social networks the role of costs benefits and reciprocity")
  Member "de retibus socialibus et legibus momenti":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nepotistic relationships in twitter and their impact on rank prestige algorithms")
  Member "influence and passivity in social media":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership")

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)