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 "topology comparison of twitter diffusion networks reliably reveals disinformation news"):
  Abstract: In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that disinformation spreads faster, deeper and more broadly than the truth on social media, where bots and echo chambers play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of disinformation stories. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these features allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream rather than disinformation news tend to shape diffusion networks with subtle yet systematic differences. This opens the way to promptly and correctly identifying disinformation on social media by solely inspecting the resulting diffusion networks.

Includes members:
  Member "false news on social media a data driven survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "influence of fake news in twitter during the 2016 us presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "an information theoretic all scales approach to comparing networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "anatomy of an online misinformation network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "the spread of low credibility content by social bots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "feature selection a data perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "hoaxy a platform for tracking online misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")

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)