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: "satirical news detection and analysis using attention mechanism and linguistic features"
  Abstract: Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.

Connected through hyperedge HE-1 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "robust lexical features for improved neural network named entity recognition"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-2 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "sanity checks for saliency maps"
    - "learning structured text representations"
    - "attention is all you need"
    - "neural discourse structure for text categorization"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-3 (induced by source paper "adversarial training for satire detection controlling for confounding variables"):
  with members:
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "automated fact checking task formulations methods and future directions"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "a structured self attentive sentence embedding"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "censoring representations with an adversary"

Connected through hyperedge HE-4 (induced by source paper "do sentence interactions matter leveraging sentence level representations for fake news classification"):
  with members:
    - "adversarial training for satire detection controlling for confounding variables"
    - "attention is all you need"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "convolutional neural networks for sentence classification"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a deep ensemble framework for fake news detection and classification"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "attention for detecting unreliable news in the information age"
    - "csi a hybrid deep model for fake news detection"
    - "automatic sarcasm detection a survey"
    - "distributed representations of words and phrases and their compositionality"

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)