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: "still out there modeling and identifying russian troll accounts on twitter"
  Abstract: There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today. Additional analysis shows that they are not merely software-controlled bots, and manage their online identities in various complex ways. Finally, we argue that if it is possible to discover these accounts using externally - accessible data, then the platforms - with access to a variety of private internal signals - should succeed at similar or better rates.

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"
    - "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"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "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 "red bots do it better comparative analysis of social bot partisan behavior"):
  with members:
    - "perils and challenges of social media and election manipulation analysis the 2018 us midterms"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "arming the public with artificial intelligence to counter social bots"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "measuring bot and human behavioral dynamics"
    - "who falls for online political manipulation"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-3 (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"
    - "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"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "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-4 (induced by source paper "textrolls identifying russian trolls on twitter from a textual perspective"):
  with members:
    - "partisanship propaganda and post truth politics quantifying impact in online debate"
    - "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"
    - "tweet2vec character based distributed representations for social media"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"

Connected through hyperedge HE-5 (induced by source paper "towards ethical content based detection of online influence campaigns"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "native language cognate effects on second language lexical choice"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"):
  with members:
    - "hunting for troll comments in news community forums"
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "an army of me sockpuppets in online discussion communities"
    - "what s in a name understanding profile name reuse on twitter"
    - "has the online discussion been manipulated quantifying online discussion authenticity within online social media"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

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)