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 "fine grained analysis of propaganda in news articles"):
  Abstract: Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.

Includes members:
  Member "proppy a system to unmask propaganda in online news":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection")
  Member "sampling the news producers a large news and feature data set for the study of the complex media landscape":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nela gt 2019 a large multi labelled news dataset for the study of misinformation in news articles")
  Member "before name calling dynamics and triggers of ad hominem fallacies in web argumentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is attention interpretable")
  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 "argotario computational argumentation meets serious games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection")
  Member "introduction to the conll 2003 shared task language independent named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "realm retrieval augmented language model pre training")
  Member "introduction to the conll 2002 shared task language independent named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "xpersona evaluating multilingual personalized chatbot")

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)