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: "who let the trolls out towards understanding state sponsored trolls"
  Abstract: Over the past few years, extensive anecdotal evidence emerged that suggests the involvement of state-sponsored actors (or "trolls") in online political campaigns with the goal to manipulate public opinion and sow discord. Recently, Twitter and Reddit released ground truth data about Russian and Iranian state-sponsored actors that were active on their platforms. In this paper, we analyze these ground truth datasets across several axes to understand how these actors operate, how they evolve over time, who are their targets, how their strategies changed over time, and what is their influence to the Web's information ecosystem. Among other things we find: a) campaigns of these actors were influenced by real-world events; b) these actors were employing different tactics and had different targets over time, thus their automated detection is not straightforward; and c) Russian trolls were clearly pro-Trump, whereas Iranian trolls were anti-Trump. Finally, using Hawkes Processes, we quantified the influence that these actors had to four Web communities: Reddit, Twitter, 4chan's Politically Incorrect board (/pol/), and Gab, finding that Russian trolls were more influential than Iranians with the exception of /pol/.

Connected through hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning"):
  with members:
    - "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "arming the public with artificial intelligence to counter social bots"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "characterizing the 2016 russian ira influence campaign"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"

Connected through hyperedge HE-2 (induced by source paper "the pushshift reddit dataset"):
  with members:
    - "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"
    - "towards ethical content based detection of online influence campaigns"
    - "content removal as a moderation strategy compliance and other outcomes in the changemyview community"
    - "are all successful communities alike characterizing and predicting the success of online communities"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "tracing community genealogy how new communities emerge from the old"

Connected through hyperedge HE-3 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"
    - "on the origins of memes by means of fringe web communities"

Connected through hyperedge HE-4 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  with members:
    - "social network analysis for social neuroscientists"
    - "its all in a name detecting and labeling bots by their name"
    - "centrality in modular networks"
    - "a quantitative approach to understanding online antisemitism"
    - "on the origins of memes by means of fringe web communities"
    - "a long term analysis of polarization on twitter"
    - "botornot a system to evaluate social bots"
    - "think locally act locally detection of small medium sized and large communities in large networks"

Connected through hyperedge HE-5 (induced by source paper "still out there modeling and identifying russian troll accounts on twitter"):
  with members:
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "unsupervised machine learning of open source russian twitter data reveals global scope and operational characteristics"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "who falls for online political manipulation"
    - "the spread of low credibility content by social bots"
    - "the spread of fake news by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-6 (induced by source paper "analysing russian trolls via nlp tools"):
  with members:
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "partisanship propaganda and post truth politics quantifying impact in online debate"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "the spread of low credibility content by social bots"
    - "an army of me sockpuppets in online discussion communities"
    - "the spread of fake news by social bots"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-7 (induced by source paper "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"):
  with members:
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "a quantitative approach to understanding online antisemitism"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"

Connected through hyperedge HE-8 (induced by source paper "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"):
  with members:
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "debatenight the role and influence of socialbots on twitter during the 1st u s presidential debate"
    - "an army of me sockpuppets in online discussion communities"
    - "the spread of fake news by social bots"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "efficient estimation of word representations in vector space"

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)