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: "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.

Connected through hyperedge HE-1 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"
    - "automated fact checking task formulations methods and future directions"

Connected through hyperedge HE-2 (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"
    - "proppy a system to unmask propaganda in online news"
    - "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-3 (induced by source paper "it takes nine to smell a rat neural multi task learning for check worthiness prediction"):
  with members:
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "automated fact checking task formulations methods and future directions"
    - "predicting factuality of reporting and bias of news media sources"
    - "fever a large scale dataset for fact extraction and verification"
    - "multi view models for political ideology detection of news articles"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"

Connected through hyperedge HE-4 (induced by source paper "neural architectures for fine grained propaganda detection in news"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "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-5 (induced by source paper "understanding bert performance in propaganda analysis"):
  with members:
    - "release strategies and the social impacts of language models"
    - "defending against neural fake news"
    - "how to fine tune bert for text classification"
    - "probing neural network comprehension of natural language arguments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-6 (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"
    - "proppy a system to unmask propaganda in online news"
    - "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-7 (induced by source paper "experiments in detecting persuasion techniques in the news"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "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"

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)