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: "arming the public with artificial intelligence to counter social bots"
  Abstract: The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots -- automated or semi-automated accounts designed to impersonate humans -- have been successfully exploited for these kinds of abuse. Researchers have responded by developing AI tools to arm the public in the fight against social bots. Here we review the literature on different types of bots, their impact, and detection methods. We use the case study of Botometer, a popular bot detection tool developed at Indiana University, to illustrate how people interact with AI countermeasures. A user experience survey suggests that bot detection has become an integral part of the social media experience for many users. However, barriers in interpreting the output of AI tools can lead to fundamental misunderstandings. The arms race between machine learning methods to develop sophisticated bots and effective countermeasures makes it necessary to update the training data and features of detection tools. We again use the Botometer case to illustrate both algorithmic and interpretability improvements of bot scores, designed to meet user expectations. We conclude by discussing how future AI developments may affect the fight between malicious bots and the public.

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"
    - "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"
    - "deep neural networks for bot detection"

Connected through hyperedge HE-2 (induced by source paper "how many bots are you following"):
  with members:
    - "do you really follow them automatic detection of credulous twitter users"
    - "fake news a survey of research detection methods and opportunities"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "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-3 (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"
    - "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"
    - "do social bots dream of electric sheep a categorisation of social media bot accounts"

Connected through hyperedge HE-4 (induced by source paper "information pollution by social bots"):
  with members:
    - "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"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-5 (induced by source paper "scalable and generalizable social bot detection through data selection"):
  with members:
    - "real time event detection on social data streams"
    - "rtbust exploiting temporal patterns for botnet detection on twitter"
    - "its all in a name detecting and labeling bots by their name"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "consistent individualized feature attribution for tree ensembles"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "deep neural networks for bot detection"

Connected through hyperedge HE-6 (induced by source paper "red bots do it better comparative analysis of social bot partisan behavior"):
  with members:
    - "perils and challenges of social media and election manipulation analysis the 2018 us midterms"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "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"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-7 (induced by source paper "bot electioneering volume visualizing social bot activity during elections"):
  with members:
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

Connected through hyperedge HE-8 (induced by source paper "lateral astroturfing attacks on twitter trending topics"):
  with members:
    - "detecting and characterizing lateral phishing at scale"
    - "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"
    - "reverse engineering socialbot infiltration strategies in twitter"
    - "social turing tests crowdsourcing sybil detection"

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)