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 "tensor factorization with label information for fake news detection"):
  Abstract: The buzz over the so-called "fake news" has created concerns about a degenerated media environment and led to the need for technological solutions. As the detection of fake news is increasingly considered a technological problem, it has attracted considerable research. Most of these studies primarily focus on utilizing information extracted from textual news content. In contrast, we focus on detecting fake news solely based on structural information of social networks. We suggest that the underlying network connections of users that share fake news are discriminative enough to support the detection of fake news. Thereupon, we model each post as a network of friendship interactions and represent a collection of posts as a multidimensional tensor. Taking into account the available labeled data, we propose a tensor factorization method which associates the class labels of data samples with their latent representations. Specifically, we combine a classification error term with the standard factorization in a unified optimization process. Results on real-world datasets demonstrate that our proposed method is competitive against state-of-the-art methods by implementing an arguably simpler approach.

Includes members:
  Member "in search of credible news":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting toxicity in news articles application to bulgarian")
  Member "studying fake news via network analysis detection and mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  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 "semi supervised content based detection of misinformation via tensor embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")
  Member "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "beyond news contents the role of social context for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "some like it hoax automated fake news detection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "detecting fake news with capsule neural networks")

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)