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: "discovery of the twitter bursty botnet"
  Abstract: Many Twitter users are bots. They can be used for spamming, opinion manipulation and online fraud. Recently we discovered the Star Wars botnet, consisting of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The bots were exposed because they tweeted uniformly from any location within two rectangle-shaped geographic zones covering Europe and the USA, including sea and desert areas in the zones. In this paper, we report another unusual behaviour of the Star Wars bots, that the bots were created in bursts or batches, and they only tweeted in their first few minutes since creation. Inspired by this observation, we discovered an even larger Twitter botnet, the Bursty botnet with more than 500,000 bots. Our preliminary study showed that the Bursty botnet was directly responsible for a large-scale online spamming attack in 2012. Most bot detection algorithms have been based on assumptions of `common' features that were supposedly shared by all bots. Our discovered botnets, however, do not show many of those features; instead, they were detected by their distinct, unusual tweeting behaviours that were unknown until now.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "the art of social bots a review and a refined taxonomy"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "false information on web and social media a survey"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "detecting influence campaigns in social networks using the ising model"
    - "botnet campaign detection on twitter"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-4 (induced by source paper "lobo evaluation of generalization deficiencies in twitter bot classifiers"):
  with members:
    - "poised spotting twitter spam off the beaten paths"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "paying for likes understanding facebook like fraud using honeypots"

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)