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 retrieval and analysis of star wars botnet in twitter"
  Abstract: It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the `Star Wars' botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. #R##N#The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots.We also reflect on the `unconventional' way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection.

Connected through hyperedge HE-1 (induced by source paper "deception strategies and threats for online discussions"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "soc2seq social embedding meets conversation model"
    - "reuters tracer toward automated news production using large scale social media data"
    - "early detection of promoted campaigns on social media"
    - "limited individual attention and online virality of low quality information"
    - "the fake news spreading plague was it preventable"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks for bot detection"):
  with members:
    - "the rise of jihadist propaganda on social networks"
    - "the spread of low credibility content by social bots"
    - "ai powered social bots"
    - "contagion dynamics of extremist propaganda in social networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"

Connected through hyperedge HE-3 (induced by source paper "measuring bot and human behavioral dynamics"):
  with members:
    - "the spread of low credibility content by social bots"
    - "ai powered social bots"
    - "contagion dynamics of extremist propaganda in social networks"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-4 (induced by source paper "online human bot interactions detection estimation and characterization"):
  with members:
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"
    - "vaporous marketing uncovering pervasive electronic cigarette advertisements on twitter"
    - "quantifying the effect of sentiment on information diffusion in social media"

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)