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: "iest wassa 2018 implicit emotions shared task"
  Abstract: Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion - the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at this http URL

Connected through hyperedge HE-1 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gated feedback recurrent neural networks"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-2 (induced by source paper "towards multimodal emotion recognition in german speech events in cars using transfer learning"):
  with members:
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "learning word vectors for 157 languages"
    - "amobee at iest 2018 transfer learning from language models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "emotion intensities in tweets"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "distant supervision for emotion detection using facebook reactions"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-3 (induced by source paper "dens a dataset for multi class emotion analysis"):
  with members:
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "practical text classification with large pre trained language models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "inferring networks of substitutable and complementary products"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "emotion recognition for vietnamese social media text"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "deep learning versus traditional classifiers on vietnamese students feedback corpus"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "a factoid question answering system for vietnamese"
    - "from word segmentation to pos tagging for vietnamese"
    - "emotion intensities in tweets"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "amobee at iest 2018 transfer learning from language models"):
  with members:
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "universal sentence encoder"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "an empirical analysis of the role of amplifiers downtoners and negations in emotion classification in microblogs"):
  with members:
    - "wassa 2017 shared task on emotion intensity"
    - "the effect of negators modals and degree adverbs on sentiment composition"
    - "crowdsourcing a word emotion association lexicon"
    - "thumbs up sentiment classification using machine learning techniques"

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)