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: "spotting rumors via novelty detection"
  Abstract: Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of features based on novelty, tailored to detect rumours early on. To compensate for the absence of repeated signals, we make use of news wire as an additional data source. Unconfirmed (novel) information with respect to the news articles is considered as an indication of rumours. Additionally we introduce pseudo feedback, which assumes that documents that are similar to previous rumours, are more likely to also be a rumour. Comparison with other real-time approaches shows that novelty based features in conjunction with pseudo feedback perform significantly better, when detecting rumours instantly after their publication.

Connected through hyperedge HE-1 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"

Connected through hyperedge HE-2 (induced by source paper "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "false information on web and social media a survey"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "fake news mitigation via point process based intervention"

Connected through hyperedge HE-3 (induced by source paper "rumor detection on social media datasets methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "automatic rumor detection on microblogs a survey"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-4 (induced by source paper "detection and resolution of rumours in social media a survey"):
  with members:
    - "rumor detection and classification for twitter data"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "simple open stance classification for rumour analysis"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "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)