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: "predicting factuality of reporting and bias of news media sources"
  Abstract: We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. These are under-studied but arguably important research problems, both in their own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news medium, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baselines, and confirm the importance of each feature type.

Connected through hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts"):
  with members:
    - "privacy aware recommendation with private attribute protection using adversarial learning"
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "detection of violent extremists in social media"
    - "detecting pathogenic social media accounts without content or network structure"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"

Connected through hyperedge HE-2 (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-3 (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"
    - "in search of credible news"
    - "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"
    - "universal sentence encoder"

Connected through hyperedge HE-4 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "impact of social media posts in real life violence a case study in bangladesh"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "who falls for online political manipulation"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "a tutorial on modeling and analysis of dynamic social networks part i"

Connected through hyperedge HE-5 (induced by source paper "credibility based fake news detection"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "network based fake news detection a pattern driven approach"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "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"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "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-8 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators"):
  with members:
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "quootstrap scalable unsupervised extraction of quotation speaker pairs from large news corpora via bootstrapping"
    - "fake news detection on social media a data mining perspective"
    - "learning to identify ambiguous and misleading news headlines"
    - "hoaxy a platform for tracking online misinformation"
    - "stance and sentiment in tweets"
    - "computational fact checking from knowledge networks"
    - "characterizing the life cycle of online news stories using social media reactions"

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.IR(Information Retrieval)