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: "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
  Abstract: In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.

Connected through hyperedge HE-1 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "constructing a word similarity graph from vector based word representation for named entity recognition"
    - "comparative study of cnn and rnn for natural language processing"
    - "exploring different dimensions of attention for uncertainty detection"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "learning text representation using recurrent convolutional neural network with highway layers"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "robust lexical features for improved neural network named entity recognition"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-3 (induced by source paper "an embarrassingly simple approach for transfer learning from pretrained language models"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-4 (induced by source paper "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"):
  with members:
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "automatically identifying complaints in social media"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "nowcasting the stance of social media users in a sudden vote the case of the greek referendum"
    - "adam a method for stochastic optimization"
    - "a computational approach to politeness with application to social factors"
    - "crowdsourcing a word emotion association lexicon"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "semeval 2017 task 4 sentiment analysis in twitter"):
  with members:
    - "amobee at semeval 2017 task 4 deep learning system for sentiment detection on twitter"
    - "putting things in context community specific embedding projections for sentiment analysis"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "a machine learning approach for opinion holder extraction in arabic language"

Connected through hyperedge HE-7 (induced by source paper "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"):
  with members:
    - "mining the relationship between emoji usage patterns and personality"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "sense embedding learning for word sense induction"
    - "emoji2vec learning emoji representations from their description"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "sentiment of emojis"

Connected through hyperedge HE-8 (induced by source paper "evomsa a multilingual evolutionary approach for sentiment analysis"):
  with members:
    - "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"
    - "ug18 at semeval 2018 task 1 generating additional training data for predicting emotion intensity in spanish"
    - "learning word vectors for 157 languages"
    - "eitaka at semeval 2018 task 1 an ensemble of n channels convnet and xgboost regressors for emotion analysis of tweets"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "niletmrg at semeval 2017 task 4 arabic sentiment analysis"
    - "are word embedding based features useful for sarcasm detection"

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)