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: "fake news detection on social media using geometric deep learning"
  Abstract: Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.

Connected through hyperedge HE-1 (induced by source paper "a model based derivative free approach to black box adversarial examples bobyqa"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "square attack a query efficient black box adversarial attack via random search"
    - "stochastic activation pruning for robust adversarial defense"
    - "darts deceiving autonomous cars with toxic signs"
    - "low frequency adversarial perturbation"
    - "robust physical world attacks on deep learning models"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

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"
    - "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"
    - "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"
    - "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"
    - "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 "fake news early detection a theory driven model"):
  with members:
    - "xfake explainable fake news detector with visualizations"
    - "open issues in combating fake news interpretability as an opportunity"
    - "fake news a survey of research detection methods and opportunities"
    - "a survey on natural language processing for fake news detection"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "automatic detection of fake news"

Connected through hyperedge HE-5 (induced by source paper "fake news detection using deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ti cnn convolutional neural networks for fake news detection"
    - "attention is all you need"

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)