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: "polarization and fake news early warning of potential misinformation targets"
  Abstract: Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, "predicting" future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.

Connected through hyperedge HE-1 (induced by source paper "a multi layer approach to disinformation detection on twitter"):
  with members:
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "fake news detection on social media using geometric deep learning"
    - "defending against neural fake news"
    - "anatomy of an online misinformation network"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "the spread of low credibility content by social bots"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-2 (induced by source paper "the covid 19 social media infodemic"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "polarization of the vaccination debate on facebook"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "modeling the infectiousness of twitter hashtags"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (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"
    - "topology comparison of twitter diffusion networks reliably reveals disinformation news"
    - "anatomy of an online misinformation network"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks"):
  with members:
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "spread of information with confirmation bias in cyber social networks"
    - "fake news in social networks"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "network inference from consensus dynamics"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-5 (induced by source paper "mis information operations an integrated perspective"):
  with members:
    - "rtbust exploiting temporal patterns for botnet detection on twitter"
    - "polarization rank a study on european news consumption on facebook"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "public discourse and news consumption on online social media a quantitative cross platform analysis of the italian referendum"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "debunking in a world of tribes"
    - "emotional dynamics in the age of misinformation"

Connected through hyperedge HE-6 (induced by source paper "the limited reach of fake news on twitter during 2019 european elections"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "misinformation spreading on facebook"
    - "the anatomy of brexit debate on facebook"
    - "science vs conspiracy collective narratives in the age of 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)