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: "word affect intensities"
  Abstract: Words often convey affect -- emotions, feelings, and attitudes. Lexicons of word-affect association have applications in automatic emotion analysis and natural language generation. However, existing lexicons indicate only coarse categories of affect association. Here, for the first time, we create an affect intensity lexicon with real-valued scores of association. We use a technique called best-worst scaling that improves annotation consistency and obtains reliable fine-grained scores. The lexicon includes terms common from both general English and terms specific to social media communications. It has close to 6,000 entries for four basic emotions. We will be adding entries for other affect dimensions shortly.

Connected through hyperedge HE-1 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-2 (induced by source paper "attention based modeling for emotion detection and classification in textual conversations"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "emotxt a toolkit for emotion recognition from text"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "deep biaffine attention for neural dependency parsing"
    - "pointer sentinel mixture models"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "joint emotion label space modelling for affect lexica"):
  with members:
    - "combining sentiment lexica with a multi view variational autoencoder"
    - "learning neural emotion analysis from 100 observations the surprising effectiveness of pre trained word representations"
    - "emotion representation mapping for automatic lexicon construction mostly performs on human level"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-4 (induced by source paper "emotion cognizance improves fake news identification"):
  with members:
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "examining gender and race bias in two hundred sentiment analysis systems"):
  with members:
    - "mitigating unwanted biases with adversarial learning"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "wassa 2017 shared task on emotion intensity"
    - "semantics derived automatically from language corpora contain human like biases"
    - "inclusivefacenet improving face attribute detection with race and gender diversity"
    - "demographic dialectal variation in social media a case study of african american english"
    - "satisfying real world goals with dataset constraints"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-6 (induced by source paper "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"):
  with members:
    - "wassa 2017 shared task on emotion intensity"
    - "analysis of italian word embeddings"
    - "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-7 (induced by source paper "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "wassa 2017 shared task on emotion intensity"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "skip thought vectors"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-8 (induced by source paper "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"):
  with members:
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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)