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 "network based fake news detection a pattern driven approach"):
  Abstract: Fake news gains has gained significant momentum, strongly motivating the need for fake news research. Many fake news detection approaches have thus been proposed, where most of them heavily rely on news content. However, network-based clues revealed when analyzing news propagation on social networks is an information that has hardly been comprehensively explored or used for fake news detection. We bridge this gap by proposing a network-based pattern-driven fake news detection approach. We aim to study the patterns of fake news in social networks, which refer to the news being spread, spreaders of the news and relationships among the spreaders. Empirical evidence and interpretations on the existence of such patterns are provided based on social psychological theories. These patterns are then represented at various network levels (i.e., node-level, ego-level, triad-level, community-level and the overall network) for being further utilized to detect fake news. The proposed approach enhances the explainability in fake news feature engineering. Experiments conducted on real-world data demonstrate that the proposed approach can outperform the state of the arts.

Includes members:
  Member "fake news early detection a theory driven model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe similarity aware multi modal fake news detection")
  Member "learning hierarchical discourse level structure for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe similarity aware multi modal fake news detection")
  Member "fake news a survey of research detection methods and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "a survey on natural language processing for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "fakedetector effective fake news detection with deep diffusive neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hgat hierarchical graph attention network for fake news detection")
  Member "the spread of low credibility content by social bots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "ti cnn convolutional neural networks for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safe similarity aware multi modal fake news detection")
  Member "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hgat hierarchical graph attention network for fake news detection")

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)