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: "deception strategies and threats for online discussions"
  Abstract: Communication plays a major role in social systems. Effective communications, which requires the transmission of messages between individuals without disruptions or noise, can be a powerful tool to deliver intended impact. Language and style of content can be leveraged to deceive and manipulate recipients. These deception and persuasion strategies can be applied to exert power and amass capital in politics and business. In this work, we provide a modest review of how such deception and persuasion strategies were applied to different communication channels over time. We provide examples of campaigns that occurred over the last century, together with their corresponding dissemination media. In the Internet age, we enjoy access to a vast amount of information and an ability to communicate without borders. However, malicious actors work toward abusing online systems to disseminate disinformation, disrupt communication, and manipulate individuals, with automated tools such as social bots. It is important to study traditional practices of persuasion in order to investigate modern procedures and tools. We provide a discussion of current threats against society while drawing parallels with historical practices and recent research on systems of detection and prevention.

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"
    - "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"
    - "discovery of the twitter bursty botnet"

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)