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: "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"
  Abstract: Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads1 from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.

Connected through hyperedge HE-1 (induced by source paper "beyond content analysis detecting targeted ads via distributed counting"):
  with members:
    - "tracing information flows between ad exchanges using retargeted ads"
    - "myadchoices bringing transparency and control to online advertising"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "efficient private statistics with succinct sketches"
    - "i always feel like somebody s watching me measuring online behavioural advertising"
    - "adscape harvesting and analyzing online display ads"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "xray enhancing the web s transparency with differential correlation"

Connected through hyperedge HE-2 (induced by source paper "can whatsapp counter misinformation by limiting message forwarding"):
  with members:
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "whatsapp doc a first look at whatsapp public group data"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "navigating the massive world of reddit using backbone networks to map user interests in social media"
    - "the role of social networks in information diffusion"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-3 (induced by source paper "ad delivery algorithms the hidden arbiters of political messaging"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "an analysis of united states online political advertising transparency"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

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)