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 "fake news detection using deep learning"):
  Abstract: The evolution of the information and communication technologies has dramatically increased the number of people with access to the Internet, which has changed the way the information is consumed. As a consequence of the above, fake news have become one of the major concerns because its potential to destabilize governments, which makes them a potential danger to modern society. An example of this can be found in the US. electoral campaign, where the term "fake news" gained great notoriety due to the influence of the hoaxes in the final result of these. In this work the feasibility of applying deep learning techniques to discriminate fake news on the Internet using only their text is studied. In order to accomplish that, three different neural network architectures are proposed, one of them based on BERT, a modern language model created by Google which achieves state-of-the-art results.

Includes members:
  Member "influence of fake news in twitter during the 2016 us presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "studying fake news via network analysis detection and mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "the rise of guardians fact checking url recommendation to combat fake news":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "ti cnn convolutional neural networks for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe similarity aware multi modal fake news detection")
  Member "recent trends in deep learning based natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "stop clickbait detecting and preventing clickbaits in online news media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the chameleon attack manipulating content display in online social media")

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)