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 clickbait challenge 2017 towards a regression model for clickbait strength"
  Abstract: Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clickbait messages. Machine learning technology may help to handle this problem, giving rise to automatic clickbait detection. To accelerate progress in this direction, we organized the Clickbait Challenge 2017, a shared task inviting the submission of clickbait detectors for a comparative evaluation. A total of 13 detectors have been submitted, achieving significant improvements over the previous state of the art in terms of detection performance. Also, many of the submitted approaches have been published open source, rendering them reproducible, and a good starting point for newcomers. While the 2017 challenge has passed, we maintain the evaluation system and answer to new registrations in support of the ongoing research on better clickbait detectors.

Connected through hyperedge HE-1 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "emo2vec learning generalized emotion representation by multi task training"

Connected through hyperedge HE-2 (induced by source paper "using neural network for identifying clickbaits in online news media"):
  with members:
    - "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"
    - "fishing for clickbaits in social images and texts with linguistically infused neural network models"

Connected through hyperedge HE-3 (induced by source paper "reinforced co training"):
  with members:
    - "clickbait detection in tweets using self attentive network"
    - "deep reinforcement learning that matters"
    - "learning to identify ambiguous and misleading news headlines"
    - "learning to learn from noisy web videos"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "neural architecture search with reinforcement learning"
    - "efficient character level document classification by combining convolution and recurrent layers"

Connected through hyperedge HE-4 (induced by source paper "clickbait detection in tweets using self attentive network"):
  with members:
    - "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"
    - "learning to identify ambiguous and misleading news headlines"
    - "a structured self attentive sentence embedding"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "fishing for clickbaits in social images and texts with linguistically infused neural network models"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "automatic detection of fake news"
    - "deep residual learning for image recognition"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "order embeddings of images and language"
    - "adam a method for stochastic optimization"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "detecting clickbait in online social media you won t believe how we did it"):
  with members:
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in 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"
    - "some like it hoax automated fake news detection in social networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-7 (induced by source paper "clickbait identification using neural networks"):
  with members:
    - "entity embeddings of categorical variables"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "a two level classification approach for detecting clickbait posts using text based features"):
  with members:
    - "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"
    - "we used neural networks to detect clickbaits you won t believe what happened next"

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)