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: "we used neural networks to detect clickbaits you won t believe what happened next"
  Abstract: Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.

Connected through hyperedge HE-1 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"

Connected through hyperedge HE-2 (induced by source paper "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "false information on web and social media a survey"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "fake news mitigation via point process based intervention"

Connected through hyperedge HE-3 (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"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (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"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"

Connected through hyperedge HE-5 (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"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "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-6 (induced by source paper "identifying clickbait a multi strategy approach using neural networks"):
  with members:
    - "clickbait detection in tweets using self attentive network"
    - "clickbait identification using neural networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "deep recurrent neural networks for product attribute extraction in ecommerce"):
  with members:
    - "attribute extraction from product titles in ecommerce"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "the clickbait challenge 2017 towards a regression model for clickbait strength"):
  with members:
    - "machine learning based detection of clickbait posts in social media"
    - "stop clickbait detecting and preventing clickbaits in online news media"

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)