human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "using neural network for identifying clickbaits in online news media"):
  Abstract: Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.

Includes members:
  Member "the clickbait challenge 2017 towards a regression model for clickbait strength":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning")
  Member "identifying clickbait a multi strategy approach using neural networks":
    also connected through other hyperedges:
      (none)
  Member "clickbait detection in tweets using self attentive network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "federated hierarchical hybrid networks for clickbait detection")
  Member "a neural clickbait detection engine":
    also connected through other hyperedges:
      (none)
  Member "identifying clickbait posts on social media with an ensemble of linear models":
    also connected through other hyperedges:
      (none)
  Member "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fake news early detection a theory driven model")
  Member "diving deep into clickbaits who use them to what extents in which topics with what effects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards reliable online clickbait video detection a content agnostic approach")
  Member "clickbait identification using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards reliable online clickbait video detection a content agnostic approach")

Question: Please tell me which class the source paper 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)