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: "a large self annotated corpus for sarcasm"
  Abstract: We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements -- 10 times more than any previous dataset -- and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthermore self-annotated -- sarcasm is labeled by the author, not an independent annotator -- and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm detection, and evaluate baseline methods.

Connected through hyperedge HE-1 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model"):
  with members:
    - "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-2 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation"):
  with members:
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "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"
    - "detecting and explaining causes from text for a time series event"
    - "automated hate speech detection and the problem of offensive language"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-3 (induced by source paper "word embeddings for sentiment analysis a comprehensive empirical survey"):
  with members:
    - "quality of word embeddings on sentiment analysis tasks"
    - "image based recommendations on styles and substitutes"
    - "retrofitting word vectors to semantic lexicons"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "a convolutional neural network for modelling sentences"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "deep and dense sarcasm detection"):
  with members:
    - "cascade contextual sarcasm detection in online discussion forums"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "very deep convolutional networks for text classification"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "isarcasm a dataset of intended sarcasm"):
  with members:
    - "cascade contextual sarcasm detection in online discussion forums"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "sarcasm analysis using conversation context"):
  with members:
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "conversation modeling on reddit using a graph structured lstm"
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "reasoning with sarcasm by reading in between"):
  with members:
    - "attention is all you need"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "representing social media users for sarcasm detection"):
  with members:
    - "reasoning with sarcasm by reading in between"
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "advances in pre training distributed word representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "exploiting social network structure for person to person sentiment analysis"

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)