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 "characterizing the 2016 russian ira influence campaign"):
  Abstract: Until recently, social media were seen to promote democratic discourse on social and political issues. However, this powerful communication ecosystem has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of, among other things, using trolls (malicious accounts created for the purpose of manipulation) and bots (automated accounts) to spread propaganda and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset of 13 million election-related posts shared on Twitter in the year of 2016 by over a million distinct users. This dataset includes accounts associated with the identified Russian trolls as well as users sharing posts in the same time period on a variety of topics around the 2016 elections. We use label propagation to infer the users' ideology based on the news sources they share. We are able to classify a large number of users as liberal or conservative with precision and recall above 84%. Conservative users who retweet Russian trolls produced significantly more tweets than liberal ones, about 8 times as many in terms of tweets. Additionally, trolls' position in the retweet network is stable over time, unlike users who retweet them who form the core of the election-related retweet network by the end of 2016. Using state-of-the-art bot detection techniques, we estimate that about 5% and 11% of liberal and conservative users are bots, respectively.

Includes members:
  Member "senator we sell ads analysis of the 2016 russian facebook ads campaign":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "deep neural networks for bot detection":
    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 social spam and the effect of bots on information diffusion in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the art of social bots a review and a refined taxonomy")
  Member "sensiblesleep a bayesian model for learning sleep patterns from smartphone events":
    also connected through other hyperedges:
      (none)
  Member "disinformation and social bot operations in the run up to the 2017 french presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "early detection of promoted campaigns on social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")

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)