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: "mitigating confirmation bias on twitter by recommending opposing views"
  Abstract: In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives. Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump.

Connected through hyperedge HE-1 (induced by source paper "can celebrities burst your bubble"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-2 (induced by source paper "open issues in combating fake news interpretability as an opportunity"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "beyond news contents the role of social context for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-3 (induced by source paper "combating fake news with interpretable news feed algorithms"):
  with members:
    - "mesonet a compact facial video forgery detection network"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "detection and resolution of rumours in social media a survey"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "a stylometric inquiry into hyperpartisan and fake news"

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)