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: "modelling context with user embeddings for sarcasm detection in social media"
  Abstract: We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.

Connected through hyperedge HE-1 (induced by source paper "a transformer based approach to irony and sarcasm detection"):
  with members:
    - "semeval 2014 task 9 sentiment analysis in twitter"
    - "spanbert improving pre training by representing and predicting spans"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "a survey on sentiment and emotion analysis for computational literary studies"
    - "deep contextualized word representations for detecting sarcasm and irony"

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"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "are word embedding based features useful for sarcasm detection"

Connected through hyperedge HE-3 (induced by source paper "author profiling for hate speech detection"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "deep learning for user comment moderation"
    - "utcnn a deep learning model of stance classificationon on social media text"
    - "node2vec scalable feature learning for networks"
    - "ex machina personal attacks seen at scale"
    - "toward socially infused information extraction embedding authors mentions and entities"

Connected through hyperedge HE-4 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity"):
  with members:
    - "exploring author context for detecting intended vs perceived sarcasm"
    - "towards multimodal sarcasm detection an _obviously_ perfect paper"
    - "sarcasm analysis using conversation context"
    - "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"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"

Connected through hyperedge HE-5 (induced by source paper "multi interest network with dynamic routing for recommendation at tmall"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "billion scale similarity search with gpus"
    - "dynamic routing between capsules"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-6 (induced by source paper "attention based neural architecture for rumor detection with author context awareness"):
  with members:
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "call attention to rumors deep attention based recurrent neural networks for early rumor detection"
    - "quantifying mental health from social media with neural user embeddings"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "csi a hybrid deep model for fake news detection"
    - "applications of online deep learning for crisis response using social media information"
    - "stance detection with bidirectional conditional encoding"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "fusion strategies for learning user embeddings with neural networks"):
  with members:
    - "an overview of computational approaches for analyzing interpretation"
    - "methods for interpreting and understanding deep neural networks"
    - "mutan multimodal tucker fusion for visual question answering"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "quantifying mental health from social media with neural user embeddings"
    - "learning feed forward one shot learners"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "social media based user embedding a literature review"):
  with members:
    - "attributed social network embedding"
    - "twitter user geolocation using deep multiview learning"
    - "quantifying mental health from social media with neural user embeddings"
    - "what the language you tweet says about your occupation"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

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)