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: "diving deep into clickbaits who use them to what extents in which topics with what effects"
  Abstract: The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

Connected through hyperedge HE-1 (induced by source paper "towards reliable online clickbait video detection a content agnostic approach"):
  with members:
    - "the age of social sensing"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "clickbait identification using neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "federated hierarchical hybrid networks for clickbait detection"):
  with members:
    - "federated reinforcement learning"
    - "federated machine learning concept and applications"
    - "clickbait detection in tweets using self attentive network"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "learning to identify ambiguous and misleading news headlines"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "bag of tricks for efficient text classification"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-3 (induced by source paper "examining the role of clickbait headlines to engage readers with reliable health related information"):
  with members:
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-4 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "clickbait detection in tweets using self attentive network"

Connected through hyperedge HE-5 (induced by source paper "detecting incongruity between news headline and body text via a deep hierarchical encoder"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "learning to identify ambiguous and misleading news headlines"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "xgboost a scalable tree boosting system"
    - "breaking the news first impressions matter on online news"

Connected through hyperedge HE-6 (induced by source paper "using neural network for identifying clickbaits in online news media"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "identifying clickbait a multi strategy approach using neural networks"
    - "clickbait detection in tweets using self attentive network"
    - "a neural clickbait detection engine"
    - "identifying clickbait posts on social media with an ensemble of linear models"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "clickbait identification using neural networks"
    - "fishing for clickbaits in social images and texts with linguistically infused neural network models"

Connected through hyperedge HE-7 (induced by source paper "semi supervised confidence network aided gated attention based recurrent neural network for clickbait detection"):
  with members:
    - "adversarial labeling for learning without labels"
    - "how curiosity can be modeled for a clickbait detector"
    - "clickbait detection in tweets using self attentive network"
    - "clickbait click based accelerated incremental training of convolutional neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "differences between health related news articles from reliable and unreliable media"):
  with members:
    - "fake cures user centric modeling of health misinformation in social media"
    - "catching zika fever application of crowdsourcing and machine learning for tracking health misinformation on twitter"
    - "fake news detection on social media a data mining perspective"

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)