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: "knowledge based trust estimating the trustworthiness of web sources"
  Abstract: The quality of web sources has been traditionally evaluated using exogenous signals such as the hyperlink structure of the graph. We propose a new approach that relies on endogenous signals, namely, the correctness of factual information provided by the source. A source that has few false facts is considered to be trustworthy. The facts are automatically extracted from each source by information extraction methods commonly used to construct knowledge bases. We propose a way to distinguish errors made in the extraction process from factual errors in the web source per se, by using joint inference in a novel multi-layer probabilistic model. We call the trustworthiness score we computed Knowledge-Based Trust (KBT). On synthetic data, we show that our method can reliably compute the true trustworthiness levels of the sources. We then apply it to a database of 2.8B facts extracted from the web, and thereby estimate the trustworthiness of 119M webpages. Manual evaluation of a subset of the results confirms the effectiveness of the method.

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 "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

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"
    - "predicting factuality of reporting and bias of news media sources"

Connected through hyperedge HE-4 (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"
    - "multi view models for political ideology detection of news articles"
    - "fact checking in community forums"

Connected through hyperedge HE-5 (induced by source paper "datapop knowledge base population using distributed voice enabled devices"):
  with members:
    - "speech is 3x faster than typing for english and mandarin text entry on mobile devices"

Connected through hyperedge HE-6 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "clickbait detection in tweets using self attentive network"

Connected through hyperedge HE-7 (induced by source paper "misinformation spreading on facebook"):
  with members:
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "emotional dynamics in the age of misinformation"
    - "computational fact checking from knowledge networks"
    - "tweetcred real time credibility assessment of content on twitter"
    - "can cascades be predicted"
    - "viral misinformation the role of homophily and polarization"

Connected through hyperedge HE-8 (induced by source paper "automated fact checking task formulations methods and future directions"):
  with members:
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "finding deceptive opinion spam by any stretch of the imagination"

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.DB(Databases)