human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "hoaxitaly a collection of italian disinformation and fact checking stories shared on twitter in 2019"):
  with members:
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"
    - "do you really follow them automatic detection of credulous twitter users"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-2 (induced by source paper "investigating italian disinformation spreading on twitter in the context of 2019 european elections"):
  with members:
    - "false news on social media a data driven survey"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "polarization and fake news early warning of potential misinformation targets"
    - "the spread of low credibility content by social bots"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "hoaxy a platform for tracking online misinformation"

Question: Please tell me which class the center node 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)