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 deeper look into sarcastic tweets using deep convolutional neural networks"
  Abstract: Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an "apparently positive" sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.

Connected through hyperedge HE-1 (induced by source paper "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sentence state lstm for text representation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"

Connected through hyperedge HE-2 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection"):
  with members:
    - "nihrio at semeval 2018 task 3 a simple and accurate neural network model for irony detection in twitter"
    - "ntua slp at semeval 2018 task 3 tracking ironic tweets using ensembles of word and character level attentive rnns"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "are word embedding based features useful for sarcasm detection"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "recent trends in deep learning based personality detection"):
  with members:
    - "sentiment and sarcasm classification with multitask learning"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "deep residual learning for image recognition"
    - "pr2 a language independent unsupervised tool for personality recognition from text"

Connected through hyperedge HE-4 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "representing social media users for sarcasm detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-5 (induced by source paper "sentiment and sarcasm classification with multitask learning"):
  with members:
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "effective approaches to attention based neural machine translation"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep and dense sarcasm detection"):
  with members:
    - "cascade contextual sarcasm detection in online discussion forums"
    - "a large self annotated corpus for sarcasm"
    - "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-7 (induced by source paper "open domain event extraction and embedding for natural gas market prediction"):
  with members:
    - "forecasting economics and financial time series arima vs lstm"
    - "cevo comprehensive event ontology enhancing cognitive annotation"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a hierarchical model of reviews for aspect based 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)