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: "automatic detection of fake news"
  Abstract: The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.

Connected through hyperedge HE-1 (induced by source paper "hgat hierarchical graph attention network for fake news detection"):
  with members:
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "embedding logical queries on knowledge graphs"
    - "how powerful are graph neural networks"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-2 (induced by source paper "safe similarity aware multi modal fake news detection"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "different spirals of sameness a study of content sharing in mainstream and alternative media"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "ti cnn convolutional neural networks for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-3 (induced by source paper "detecting fake news with capsule neural networks"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "information aggregation via dynamic routing for sequence encoding"
    - "zero shot user intent detection via capsule neural networks"
    - "fake news detection in social networks via crowd signals"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "dynamic routing between capsules"

Connected through hyperedge HE-4 (induced by source paper "deceptive ai explanations creation and detection"):
  with members:
    - "human grounded evaluations of explanation methods for text classification"
    - "a survey on bias and fairness in machine learning"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"
    - "how model accuracy and explanation fidelity influence user trust"
    - "perturbation sensitivity analysis to detect unintended model biases"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-5 (induced by source paper "text similarity using word embeddings to classify misinformation"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "combating fake news a survey on identification and mitigation techniques"):
  with members:
    - "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"
    - "adversarial classification on social networks"
    - "all in one multi task learning for rumour verification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "me my echo chamber and i introspection on social media polarization"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-7 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-8 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

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.CL(Computation and Language)