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 "r fakeddit a new multimodal benchmark dataset for fine grained fake news detection"):
  Abstract: Fake news has altered society in negative ways as evidenced in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic fake news detection algorithms is an efficient way to combat the rampant dissemination of fake news. However, using an effective dataset has been a problem for fake news research and detection model development. In this paper, we present Fakeddit, a novel dataset consisting of about 800,000 samples from multiple categories of fake news. Each sample is labeled according to 2-way, 3-way, and 5-way classification categories. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at this scale and breadth. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification.

Includes members:
  Member "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ginger cannot cure cancer battling fake health news with a comprehensive data repository")
  Member "defending against neural fake news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "vatex a large scale high quality multilingual dataset for video and language research":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "normalized and geometry aware self attention network for image captioning")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "fever a large scale dataset for fact extraction and verification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)