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: "studying fake news via network analysis detection and mitigation"
  Abstract: Social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Identifying and mitigating fake news also presents unique challenges. To tackle these challenges, many existing research efforts exploit various features of the data, including network features. In essence, a news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.

Connected through hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "generalizing from a few examples a survey on few shot learning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "defending against neural fake news"
    - "hierarchical propagation networks for fake news detection investigation and exploitation"
    - "declare debunking fake news and false claims using evidence aware deep learning"

Connected through hyperedge HE-2 (induced by source paper "joint inference on truth rumor and their sources in social networks"):
  with members:
    - "automatic stance detection using end to end memory networks"
    - "necessary and sufficient budgets in information source finding with querying adaptivity gap"
    - "gemsec graph embedding with self clustering"
    - "on measuring bias in online information"
    - "fake news detection on social media a data mining perspective"
    - "rumor source detection under querying with untruthful answers"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-3 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "deepinf social influence prediction with deep learning"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-4 (induced by source paper "fake news detection on social media using geometric deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "on the convergence of adam and beyond"
    - "dual primal graph convolutional networks"
    - "fake news a survey of research detection methods and opportunities"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "a survey on computational politics"):
  with members:
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"
    - "words are malleable computing semantic shifts in political and media discourse"

Connected through hyperedge HE-6 (induced by source paper "news labeling as early as possible real or fake"):
  with members:
    - "fake news a survey of research detection methods and opportunities"
    - "learning class specific word representations for early detection of hoaxes in social media"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "detection and resolution of rumours in social media a survey"
    - "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"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-7 (induced by source paper "tensor factorization with label information for fake news detection"):
  with members:
    - "in search of credible news"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "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"
    - "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"

Connected through hyperedge HE-8 (induced by source paper "rumor detection on social media datasets methods and opportunities"):
  with members:
    - "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"
    - "spotting rumors via novelty detection"

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)