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: "multi view models for political ideology detection of news articles"
  Abstract: A news article's title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.

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"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

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"
    - "proppy a system to unmask propaganda in online news"
    - "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"
    - "fact checking in community forums"
    - "on measuring bias in online information"

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"
    - "fine grained analysis of propaganda in news articles"
    - "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"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"

Connected through hyperedge HE-4 (induced by source paper "mediarank computational ranking of online news sources"):
  with members:
    - "faithfully explaining rankings in a news recommender system"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "automatic detection of fake news"
    - "fake news detection on social media a data mining perspective"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "csi a hybrid deep model for fake news detection"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-5 (induced by source paper "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "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"
    - "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-6 (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"
    - "proppy a system to unmask propaganda in online news"
    - "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"
    - "on measuring bias in online information"
    - "automatic dialect detection in arabic broadcast speech"

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)