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: "sifting robotic from organic text a natural language approach for detecting automation on twitter"
  Abstract: Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. Due to the increasing popularity of Twitter, its perceived potential for exerting social influence has led to the rise of a diverse community of automatons, commonly referred to as bots. These inorganic and semi-organic Twitter entities can range from the benevolent (e.g., weather-update bots, help-wanted-alert bots) to the malevolent (e.g., spamming messages, advertisements, or radical opinions). Existing detection algorithms typically leverage meta-data (time between tweets, number of followers, etc.) to identify robotic accounts. Here, we present a powerful classification scheme that exclusively uses the natural language text from organic users to provide a criterion for identifying accounts posting automated messages. Since the classifier operates on text alone, it is flexible and may be applied to any textual data beyond the Twitter-sphere.

Connected through hyperedge HE-1 (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-2 (induced by source paper "textrolls identifying russian trolls on twitter from a textual perspective"):
  with members:
    - "partisanship propaganda and post truth politics quantifying impact in online debate"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "who falls for online political manipulation"
    - "tweet2vec character based distributed representations for social media"

Connected through hyperedge HE-3 (induced by source paper "boostnet bootstrapping detection of socialbots and a case study from guatemala"):
  with members:
    - "socialbots whitewashing contested elections a case study from honduras"
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "on the influence of social bots in online protests"

Connected through hyperedge HE-4 (induced by source paper "socialbots whitewashing contested elections a case study from honduras"):
  with members:
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

Connected through hyperedge HE-5 (induced by source paper "social media usage patterns during natural hazards"):
  with members:
    - "information spreading during emergencies and anomalous events"
    - "online human bot interactions detection estimation and characterization"
    - "performance of social network sensors during hurricane sandy"

Connected through hyperedge HE-6 (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"
    - "discovery retrieval and analysis of star wars botnet in twitter"

Connected through hyperedge HE-7 (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"
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botivist calling volunteers to action using online bots"

Connected through hyperedge HE-8 (induced by source paper "deploying south african social honeypots on twitter"):
  with members:
    - "a security approach based on honeypots protecting online social network from malicious profiles"
    - "online human bot interactions detection estimation and characterization"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "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.CL(Computation and Language)