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: "the spread of low credibility content by social bots"
  Abstract: The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

Connected through hyperedge HE-1 (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"
    - "anatomy of an online misinformation network"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "feature selection a data perspective"

Connected through hyperedge HE-2 (induced by source paper "uncovering coordinated networks on social media"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"

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"
    - "anatomy of an online misinformation network"
    - "polarization and fake news early warning of potential misinformation targets"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-4 (induced by source paper "information credibility in the social web contexts approaches and open issues"):
  with members:
    - "on the inevitability of online echo chambers"
    - "combating fake news a survey on identification and mitigation techniques"
    - "automated fact checking task formulations methods and future directions"
    - "automated hate speech detection and the problem of offensive language"
    - "fake news detection on social media a data mining perspective"
    - "echo chambers in the age of misinformation"

Connected through hyperedge HE-5 (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"
    - "topology comparison of twitter diffusion networks reliably reveals disinformation news"

Connected through hyperedge HE-6 (induced by source paper "unveiling coordinated groups behind white helmets disinformation"):
  with members:
    - "uncovering coordinated networks on social media"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-7 (induced by source paper "traffic networks are vulnerable to disinformation attacks"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-8 (induced by source paper "arming the public with artificial intelligence to counter social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "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"
    - "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"

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)