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: "anatomy of an online misinformation network"
  Abstract: Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"

Connected through hyperedge HE-2 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information"):
  with members:
    - "false news on social media a data driven survey"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "fake news detection on social media using geometric deep learning"
    - "an information theoretic all scales approach to comparing networks"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "the spread of low credibility content by social bots"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "feature selection a data perspective"

Connected through hyperedge HE-3 (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"
    - "polarization and fake news early warning of potential misinformation targets"
    - "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-4 (induced by source paper "the rumour mill making the spread of misinformation explicit and tangible"):
  with members:
    - "ctrl a conditional transformer language model for controllable generation"
    - "defending against neural fake news"
    - "supporting the use of user generated content in journalistic practice"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"

Connected through hyperedge HE-5 (induced by source paper "the rumour mill making misinformation spread visible and tangible"):
  with members:
    - "ctrl a conditional transformer language model for controllable generation"
    - "defending against neural fake news"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"

Connected through hyperedge HE-6 (induced by source paper "arming the public with artificial intelligence to counter social bots"):
  with members:
    - "deep neural networks for bot detection"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "deception strategies and threats for online discussions"
    - "the spread of low credibility content by social bots"
    - "a deep reinforcement learning chatbot"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "discovery of the twitter bursty botnet"

Connected through hyperedge HE-7 (induced by source paper "false news on social media a data driven survey"):
  with members:
    - "the junk news aggregator examining junk news posted on facebook starting with the 2018 us midterm elections"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "automated fact checking task formulations methods and future directions"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-8 (induced by source paper "influence of fake news in twitter during the 2016 us presidential election"):
  with members:
    - "the spread of low credibility content by social bots"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "users polarization on facebook and youtube"
    - "hoaxy a platform for tracking online misinformation"
    - "the anatomy of brexit debate on facebook"
    - "the rise of social bots"

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)