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: "the dark side of micro task marketplaces characterizing fiverr and automatically detecting crowdturfing"
  Abstract: As human computation on crowdsourcing systems has become popular and powerful for performing tasks, malicious users have started misusing these systems by posting malicious tasks, propagating manipulated contents, and targeting popular web services such as online social networks and search engines. Recently, these malicious users moved to Fiverr, a fast-growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run by crowd workers) and malicious customers can purchase those tasks for only $5. In this paper, we present a comprehensive analysis of Fiverr. First, we identify the most popular types of crowdturfing tasks found in this marketplace and conduct case studies for these crowdturfing tasks. Then, we build crowdturfing task detection classifiers to filter these tasks and prevent them from becoming active in the marketplace. Our experimental results show that the proposed classification approach effectively detects crowdturfing tasks, achieving 97.35% accuracy. Finally, we analyze the real world impact of crowdturfing tasks by purchasing active Fiverr tasks and quantifying their impact on a target site. As part of this analysis, we show that current security systems inadequately detect crowdsourced manipulation, which confirms the necessity of our proposed crowdturfing task detection approach.

Connected through hyperedge HE-1 (induced by source paper "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"):
  with members:
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "neural text generation past present and beyond"
    - "deep text classification can be fooled"
    - "simple fusion return of the language model"

Connected through hyperedge HE-2 (induced by source paper "uncovering download fraud activities in mobile app markets"):
  with members:
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "fairplay fraud and malware detection in google play"
    - "xgboost a scalable tree boosting system"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-3 (induced by source paper "unpacking the social media bot a typology to guide research and policy"):
  with members:
    - "the spread of low credibility content by social bots"
    - "ai powered social bots"
    - "do social bots dream of electric sheep a categorisation of social media bot accounts"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"

Connected through hyperedge HE-4 (induced by source paper "automated crowdturfing attacks and defenses in online review systems"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "lstm based system call language modeling and robust ensemble method for designing host based intrusion detection systems"
    - "smart reply automated response suggestion for email"
    - "neural text generation from structured data with application to the biography domain"
    - "exploring the limits of language modeling"
    - "deepdga adversarially tuned domain generation and detection"
    - "inferring networks of substitutable and complementary products"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-5 (induced by source paper "has the online discussion been manipulated quantifying online discussion authenticity within online social media"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-6 (induced by source paper "a large scale analysis of the marketplace characteristics in fiverr"):
  with members:
    - "characterizing seller driven black hat marketplaces"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-7 (induced by source paper "characterizing seller driven black hat marketplaces"):
  with members:
    - "paying for likes understanding facebook like fraud using honeypots"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

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)