human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "combating fake news with interpretable news feed algorithms"):
  Abstract: Nowadays, artificial intelligence algorithms are used for targeted and personalized content distribution in the large scale as part of the intense competition for attention in the digital media environment. Unfortunately, targeted information dissemination may result in intellectual isolation and discrimination. Further, as demonstrated in recent political events in the US and EU, malicious bots and social media users can create and propagate targeted `fake news' content in different forms for political gains. From the other direction, fake news detection algorithms attempt to combat such problems by identifying misinformation and fraudulent user profiles. This paper reviews common news feed algorithms as well as methods for fake news detection, and we discuss how news feed algorithms could be misused to promote falsified content, affect news diversity, or impact credibility. We review how news feed algorithms and recommender engines can enable confirmation bias to isolate users to certain news sources and affecting the perception of reality. As a potential solution for increasing user awareness of how content is selected or sorted, we argue for the use of interpretable and explainable news feed algorithms. We discuss how improved user awareness and system transparency could mitigate unwanted outcomes of echo chambers and bubble filters in social media.

Includes members:
  Member "mesonet a compact facial video forgery detection network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "beyond pixels image provenance analysis leveraging metadata":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning transformation aware embeddings for image forensics")
  Member "detection and resolution of rumours in social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "mitigating confirmation bias on twitter by recommending opposing views":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can celebrities burst your bubble")
  Member "quantifying search bias investigating sources of bias for political searches in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "liar liar pants on fire a new benchmark dataset for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discover your social identity from what you tweet a content based approach")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)