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: "crowdsourcing a word emotion association lexicon"
  Abstract: Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word-emotion and word-polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher inter-annotator agreement than that obtained by asking if a term evokes an emotion.

Connected through hyperedge HE-1 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "lifelong learning for sentiment classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "introducing aspects of creativity in automatic poetry generation"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "beyond narrative description generating poetry from images by multi adversarial training"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"):
  with members:
    - "towards multimodal emotion recognition in german speech events in cars using transfer learning"
    - "dens a dataset for multi class emotion analysis"
    - "semantic change and emerging tropes in a large corpus of new high german poetry"
    - "diachronic topics in new high german poetry"
    - "multi task learning for argumentation mining in low resource settings"
    - "iest wassa 2018 implicit emotions shared task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "this pin can be easily guessed"):
  with members:
    - "towards baselines for shoulder surfing on mobile authentication"
    - "text entry method affects password security"

Connected through hyperedge HE-5 (induced by source paper "a model to measure the spread power of rumors"):
  with members:
    - "a speech act classifier for persian texts and its application in identify speech act of rumors"
    - "a machine learning approach to persian text readability assessment using a crowdsourced dataset"

Connected through hyperedge HE-6 (induced by source paper "who wins the game of thrones how sentiments improve the prediction of candidate choice"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "and the winner is bayesian twitter based prediction on 2016 u s presidential election"

Connected through hyperedge HE-7 (induced by source paper "understanding the prediction mechanism of sentiments by xai visualization"):
  with members:
    - "market trend prediction using sentiment analysis lessons learned and paths forward"

Connected through hyperedge HE-8 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "a comprehensive survey of deep learning for image captioning"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

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)