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 "a kernel of truth determining rumor veracity on twitter by diffusion pattern alone"):
  Abstract: Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? #R##N#Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.

Includes members:
  Member "xfake explainable fake news detector with visualizations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fake news early detection a theory driven model")
  Member "certifiable robustness and robust training for graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  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 "analysis methods in neural language processing a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "a motif based approach for identifying controversy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "kernel graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "message passing attention networks for document understanding")
  Member "csi a hybrid deep model for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  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)