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: "proppy a system to unmask propaganda in online news"
  Abstract: We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the news into events, and organizes the articles about an event on the basis of the likelihood that they contain propagandistic content. The system is trained on known propaganda sources using a variety of stylistic features. The evaluation results on a standard dataset show state-of-the-art results for propaganda detection.

Connected through hyperedge HE-1 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection"):
  with members:
    - "fine tuned neural models for propaganda detection at the sentence and fragment levels"
    - "understanding bert performance in propaganda analysis"
    - "defending against neural fake news"
    - "neural architectures for fine grained propaganda detection in news"
    - "fine grained analysis of propaganda in news articles"
    - "roberta a robustly optimized bert pretraining approach"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "predicting factuality of reporting and bias of news media sources"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "multi view models for political ideology detection of news articles"
    - "fact checking in community forums"
    - "on measuring bias in online information"

Connected through hyperedge HE-3 (induced by source paper "neural architectures for fine grained propaganda detection in news"):
  with members:
    - "fine grained analysis of propaganda in news articles"
    - "neural relation extraction within and across sentence boundaries"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argotario computational argumentation meets serious games"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-4 (induced by source paper "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "automated fact checking task formulations methods and future directions"
    - "predicting factuality of reporting and bias of news media sources"
    - "multi view models for political ideology detection of news articles"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-5 (induced by source paper "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"):
  with members:
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting factuality of reporting and bias of news media sources"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "multi view models for political ideology detection of news articles"
    - "on measuring bias in online information"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-6 (induced by source paper "fine grained analysis of propaganda in news articles"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argotario computational argumentation meets serious games"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "fine tuned neural models for propaganda detection at the sentence and fragment levels"):
  with members:
    - "findings of the nlp4if 2019 shared task on fine grained propaganda detection"
    - "fine grained analysis of propaganda in news articles"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "a topic agnostic approach for identifying fake news pages"):
  with members:
    - "predicting factuality of reporting and bias of news media sources"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "automatic detection of fake news"
    - "fake news detection on social media a data mining perspective"

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)