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: "finding deceptive opinion spam by any stretch of the imagination"
  Abstract: Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

Connected through hyperedge HE-3 (induced by source paper "ginger cannot cure cancer battling fake health news with a comprehensive data repository"):
  with members:
    - "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
    - "propagation from deceptive news sources who shares how much how evenly and how quickly"
    - "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"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "beyond news contents the role of social context for fake news detection"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-4 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "the fact extraction and verification fever shared task"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "attention is all you need"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-5 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

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 "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"

Connected through hyperedge HE-8 (induced by source paper "defending against neural fake news"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "mask predict parallel decoding of conditional masked language models"
    - "release strategies and the social impacts of language models"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"

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)