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 sets verified users apart insights analysis and prediction of verified users on twitter"):
  Abstract: Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extending aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. #R##N#With this in mind, we seek to unravel the aspects of a user's profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle's verification status. We collected a dataset consisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sentiment in tweets and an authoritative language style are the most pertinent predictors of verification status. #R##N#To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on Twitter.

Includes members:
  Member "elites tweet characterizing the twitter verified user network":
    also connected through other hyperedges:
      (none)
  Member "election bias comparing polls and twitter in the 2016 u s election":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election")
  Member "an in depth characterisation of bots and humans on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the mass fake news and cognition security")
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "measuring emotional contagion in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bots elections and social media a brief overview")
  Member "klout score measuring influence across multiple social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "midmod osn a microscopic level information diffusion model for online social networks")
  Member "tweetcred real time credibility assessment of content on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "checkthat at clef 2020 enabling the automatic identification and verification of claims in social media")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mining social media data for biomedical signals and health related behavior")

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)