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 "open issues in combating fake news interpretability as an opportunity"):
  Abstract: Combating fake news needs a variety of defense methods. Although rumor detection and various linguistic analysis techniques are common methods to detect false content in social media, there are other feasible mitigation approaches that could be explored in the machine learning community. In this paper, we present open issues and opportunities in fake news research that need further attention. We first review different stages of the news life cycle in social media and discuss core vulnerability issues for news feed algorithms in propagating fake news content with three examples. We then discuss how complexity and unclarity of the fake news problem limit the advancements in this field. Lastly, we present research opportunities from interpretable machine learning to mitigate fake news problems with 1) interpretable fake news detection and 2) transparent news feed algorithms. We propose three dimensions of interpretability consisting of algorithmic interpretability, human interpretability, and the inclusion of supporting evidence that can benefit fake news mitigation methods in different ways.

Includes members:
  Member "combating fake news a survey on identification and mitigation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions")
  Member "a survey of evaluation methods and measures for interpretable machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  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-3 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "declare debunking fake news and false claims using evidence aware deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "fake news a survey of research detection methods and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "beyond pixels image provenance analysis leveraging metadata":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning transformation aware embeddings for image forensics")
  Member "mitigating confirmation bias on twitter by recommending opposing views":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "can celebrities burst your bubble")
  Member "beyond news contents the role of social context for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")

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)