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: "serf and turf crowdturfing for fun and profit"
  Abstract: Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses using crowd-sourcing systems. However, crowd-sourcing systems can also pose a real challenge to existing security mechanisms deployed to protect Internet services. Many of these techniques make the assumption that malicious activity is generated automatically by machines, and perform poorly or fail if users can be organized to perform malicious tasks using crowd-sourcing systems. Through measurements, we have found surprising evidence showing that not only do malicious crowd-sourcing systems exist, but they are rapidly growing in both user base and total revenue. In this paper, we describe a significant effort to study and understand these "crowdturfing" systems in today's Internet. We use detailed crawls to extract data about the size and operational structure of these crowdturfing systems. We analyze details of campaigns offered and performed in these sites, and evaluate their end-to-end effectiveness by running active, non-malicious campaigns of our own. Finally, we study and compare the source of workers on crowdturfing sites in different countries. Our results suggest that campaigns on these systems are highly effective at reaching users, and their continuing growth poses a concrete threat to online communities such as social networks, both in the US and elsewhere.

Connected through hyperedge HE-1 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

Connected through hyperedge HE-2 (induced by source paper "human and automatic detection of generated text"):
  with members:
    - "the curious case of neural text degeneration"
    - "do massively pretrained language models make better storytellers"
    - "bertscore evaluating text generation with bert"
    - "real or fake learning to discriminate machine from human generated text"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "are we safe yet the limitations of distributional features for fake news detection"

Connected through hyperedge HE-3 (induced by source paper "teevil identity lease via trusted execution environments"):
  with members:
    - "stay on topic generating context specific fake restaurant reviews"
    - "fake news detection on social media a data mining perspective"
    - "software grand exposure sgx cache attacks are practical"
    - "rollback and forking detection for trusted execution environments using lightweight collective memory"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "debunking in a world of tribes"
    - "can cascades be predicted"

Connected through hyperedge HE-4 (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"
    - "the dark side of micro task marketplaces characterizing fiverr and automatically detecting crowdturfing"
    - "paying for likes understanding facebook like fraud using honeypots"

Connected through hyperedge HE-5 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers"):
  with members:
    - "decision tree design for classification in crowdsourcing systems"
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "multi object classification via crowdsourcing with a reject option"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-6 (induced by source paper "bribery in rating system a game theoretic perspective"):
  with members:
    - "a network based rating system and its resistance to bribery"
    - "how hard is bribery in elections"

Connected through hyperedge HE-7 (induced by source paper "a decade of mal activity reporting a retrospective analysis of internet malicious activity blacklists"):
  with members:
    - "the chain of implicit trust an analysis of the web third party resources loading"
    - "paying for likes understanding facebook like fraud using honeypots"

Connected through hyperedge HE-8 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"

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)