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: "disinformation and social bot operations in the run up to the 2017 french presidential election"
  Abstract: Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation that occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts, posted between 27 April and 7 May 2017 (Election Day). We then set to study the  MacronLeaks  disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t. Prior interests of disinformation adopters pinpoint to the reasons of scarce success of this campaign: the users who engaged with  MacronLeaks  are mostly foreigners with pre-existing interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots.

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"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"

Connected through hyperedge HE-2 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts"):
  with members:
    - "privacy aware recommendation with private attribute protection using adversarial learning"
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "detection of violent extremists in social media"
    - "detecting pathogenic social media accounts without content or network structure"
    - "similar but different exploiting users congruity for recommendation systems"
    - "predicting factuality of reporting and bias of news media sources"

Connected through hyperedge HE-3 (induced by source paper "how many bots are you following"):
  with members:
    - "do you really follow them automatic detection of credulous twitter users"
    - "arming the public with artificial intelligence to counter social bots"
    - "fake news a survey of research detection methods and opportunities"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

Connected through hyperedge HE-4 (induced by source paper "uncovering coordinated networks on social media"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "the spread of low credibility content by social bots"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-5 (induced by source paper "unveiling coordinated groups behind white helmets disinformation"):
  with members:
    - "uncovering coordinated networks on social media"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-6 (induced by source paper "traffic networks are vulnerable to disinformation attacks"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-7 (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"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "arming the public with artificial intelligence to counter social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "deep neural networks for bot detection"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "deception strategies and threats for online discussions"
    - "the spread of low credibility content by social bots"
    - "a deep reinforcement learning chatbot"
    - "early detection of promoted campaigns on social media"
    - "discovery of the twitter bursty botnet"

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)