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: "in search of credible news"
  Abstract: We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources. We then propose a language-independent approach for automatically distinguishing credible from fake news, based on a rich feature set. In particular, we use linguistic (n-gram), credibility-related (capitalization, punctuation, pronoun use, sentiment polarity), and semantic (embeddings and DBPedia data) features. Our experiments on three different testsets show that our model can distinguish credible from fake news with very high accuracy.

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"
    - "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"
    - "automated fact checking task formulations methods and future directions"

Connected through hyperedge HE-2 (induced by source paper "predicting the role of political trolls in social media"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "claimrank detecting check worthy claims in arabic and english"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"
    - "an army of me sockpuppets in online discussion communities"
    - "the effect of collective attention on controversial debates on social media"

Connected through hyperedge HE-3 (induced by source paper "automatic fact checking using context and discourse information"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "claimrank detecting check worthy claims in arabic and english"
    - "automatic stance detection using end to end memory networks"
    - "integrating stance detection and fact checking in a unified corpus"

Connected through hyperedge HE-4 (induced by source paper "semeval 2019 task 8 fact checking in community question answering forums"):
  with members:
    - "hunting for troll comments in news community forums"
    - "tmlab srpol at semeval 2019 task 8 fact checking in community question answering forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "predicting factuality of reporting and bias of news media sources"
    - "universal sentence encoder"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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-6 (induced by source paper "it takes nine to smell a rat neural multi task learning for check worthiness prediction"):
  with members:
    - "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"
    - "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-7 (induced by source paper "tensor factorization with label information for fake news detection"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"

Connected through hyperedge HE-8 (induced by source paper "a context aware approach for detecting check worthy claims in political debates"):
  with members:
    - "fully automated fact checking using external sources"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "computational fact checking from knowledge networks"
    - "exploiting similarities among languages for machine translation"

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)