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: "reverse engineering socialbot infiltration strategies in twitter"
  Abstract: Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots $-$ automated accounts that mimic real users $-$ seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a $2^k$ factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  with members:
    - "social network analysis for social neuroscientists"
    - "its all in a name detecting and labeling bots by their name"
    - "centrality in modular networks"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "on the origins of memes by means of fringe web communities"
    - "a long term analysis of polarization on twitter"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-4 (induced by source paper "lateral astroturfing attacks on twitter trending topics"):
  with members:
    - "detecting and characterizing lateral phishing at scale"
    - "arming the public with artificial intelligence to counter social bots"
    - "early detection of promoted campaigns on social media"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "the rise of social botnets attacks and countermeasures"
    - "botornot a system to evaluate social bots"
    - "censorship is futile"
    - "social turing tests crowdsourcing sybil detection"

Connected through hyperedge HE-5 (induced by source paper "its all in a name detecting and labeling bots by their name"):
  with members:
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"

Connected through hyperedge HE-6 (induced by source paper "characterizing the 2016 russian ira influence campaign"):
  with members:
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "sensiblesleep a bayesian model for learning sleep patterns from smartphone events"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-7 (induced by source paper "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"):
  with members:
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "the spread of low credibility content by social bots"
    - "the spread of fake news by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"

Connected through hyperedge HE-8 (induced by source paper "life never matters in the democrats mind examining strategies of retweeted social bots during a mass shooting event"):
  with members:
    - "strategies and influence of social bots in a 2017 german state election a case study on twitter"
    - "do social bots dream of electric sheep a categorisation of social media bot accounts"
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "political bots and the manipulation of public opinion in venezuela"
    - "truthy mapping the spread of astroturf in microblog streams"

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)