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 task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
  Abstract: In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyperpartisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.

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"
    - "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 "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"
    - "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-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"
    - "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-4 (induced by source paper "fact checking meets fauxtography verifying claims about images"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fighting fake news image splice detection via learned self consistency"
    - "predicting factuality of reporting and bias of news media sources"
    - "fact checking in community forums"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "fully automated fact checking using external sources"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "leveraging joint interactions for credibility analysis in news communities"

Connected through hyperedge HE-5 (induced by source paper "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"):
  with members:
    - "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"
    - "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.IR(Information Retrieval)