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: "emotion intensities in tweets"
  Abstract: This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best--worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity, and, the extent to which two emotions are similar in terms of how they manifest in language.

Connected through hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "investigating classification techniques with feature selection for intention mining from twitter feed"):
  with members:
    - "feature selection a data perspective"
    - "characterizing linguistic attributes for automatic classification of intent based racist radicalized posts on tumblr micro blogging website"
    - "the lumiere project bayesian user modeling for inferring the goals and needs of software users"

Connected through hyperedge HE-3 (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"
    - "iest wassa 2018 implicit emotions shared task"
    - "amobee at iest 2018 transfer learning from language models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "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-4 (induced by source paper "learning word ratings for empathy and distress from document level user responses"):
  with members:
    - "joint emotion label space modelling for affect lexica"
    - "modeling empathy and distress in reaction to news stories"
    - "aff2vec affect enriched distributional word representations"
    - "annotating and modeling empathy in spoken conversations"
    - "advances in pre training distributed word representations"
    - "a unified approach to interpreting model predictions"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "affect in tweets using experts model"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (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"
    - "iest wassa 2018 implicit emotions shared task"
    - "a factoid question answering system for vietnamese"
    - "from word segmentation to pos tagging for vietnamese"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "learning neural emotion analysis from 100 observations the surprising effectiveness of pre trained word representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "learning word vectors for 157 languages"
    - "aff2vec affect enriched distributional word representations"
    - "attention is all you need"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "deep multitask learning for semantic dependency parsing"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "deepemo learning and enriching pattern based emotion representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts"
    - "emoji2vec learning emoji representations from their description"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

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)