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: "an analysis of united states online political advertising transparency"
  Abstract: During the summer of 2018, Facebook, Google, and Twitter created policies and implemented transparent archives that include U.S. political advertisements which ran on their platforms. Through our analysis of over 1.3 million ads with political content, we show how different types of political advertisers are disseminating U.S. political messages using Facebook, Google, and Twitter's advertising platforms. We find that in total, ads with political content included in these archives have generated between 8.67 billion - 33.8 billion impressions and that sponsors have spent over $300 million USD on advertising with U.S. political content. #R##N#We are able to improve our understanding of political advertisers on these platforms. We have also discovered a significant amount of advertising by quasi for-profit media companies that appeared to exist for the sole purpose of creating deceptive online communities focused on spreading political messaging and not for directly generating profits. Advertising by such groups is a relatively recent phenomenon, and appears to be thriving on online platforms due to the lower regulatory requirements compared to traditional advertising platforms. #R##N#We have found through our attempts to collect and analyze this data that there are many limitations and weaknesses that enable intentional or accidental deception and bypassing of the current implementations of these transparency archives. We provide several suggestions for how these archives could be made more robust and useful. Overall, these efforts by Facebook, Google, and Twitter have improved political advertising transparency of honest and, in some cases, possibly dishonest advertisers on their platforms. We thank the people at these companies who have built these archives and continue to improve them.

Connected through hyperedge HE-1 (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"
    - "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"
    - "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)