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: "dissecting a social botnet growth content and influence in twitter"
  Abstract: Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users.

Connected through hyperedge HE-1 (induced by source paper "detecting and characterizing bots that commit code"):
  with members:
    - "an exploratory study of bot commits"
    - "a dataset and an approach for identity resolution of 38 million author ids extracted from 2b git commits"
    - "which pull requests get accepted and why a study of popular npm packages"
    - "patterns of effort contribution and demand and user classification based on participation patterns in npm ecosystem"

Connected through hyperedge HE-2 (induced by source paper "an exploratory study of bot commits"):
  with members:
    - "detecting and characterizing bots that commit code"
    - "patterns of effort contribution and demand and user classification based on participation patterns in npm ecosystem"

Connected through hyperedge HE-3 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-4 (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"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"

Connected through hyperedge HE-5 (induced by source paper "bots elections and social media a brief overview"):
  with members:
    - "the history of digital spam"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "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"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-6 (induced by source paper "information pollution by social bots"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "the role of bot squads in the political propaganda on twitter"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "quantifying biases in online information exposure"
    - "the spread of low credibility content by social bots"
    - "how algorithmic popularity bias hinders or promotes quality"

Connected through hyperedge HE-7 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "predicting factuality of reporting and bias of news media sources"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "impact of social media posts in real life violence a case study in bangladesh"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "who falls for online political manipulation"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"

Connected through hyperedge HE-8 (induced by source paper "hateful people or hateful bots detection and characterization of bots spreading religious hatred in arabic social media"):
  with members:
    - "universal dependency parsing from scratch"
    - "mind your pov convergence of articles and editors towards wikipedia s neutrality norm"
    - "deep learning for hate speech detection in tweets"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "analyzing the targets of hate in online social media"
    - "the rise of social bots"
    - "content and network dynamics behind egyptian political polarization on twitter"

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.CY(Computers and Society)