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: "semeval 2017 task 4 sentiment analysis in twitter"
  Abstract: This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii)~we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

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"
    - "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"
    - "emotion intensities in tweets"

Connected through hyperedge HE-2 (induced by source paper "unsupervised sentiment analysis for code mixed data"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "a survey of cross lingual word embedding models"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "learning word vectors for 157 languages"
    - "sentiment analysis of code mixed languages leveraging resource rich languages"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "natural language processing sentiment analysis and clinical analytics"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "network analysis of recurring youtube spam campaigns"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-4 (induced by source paper "a multi source entity level sentiment corpus for the financial domain the finlin corpus"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (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"
    - "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"
    - "visual reasoning with multi hop feature modulation"

Connected through hyperedge HE-6 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-7 (induced by source paper "learning the difference that makes a difference with counterfactually augmented data"):
  with members:
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "what do deep networks like to read"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "breaking nli systems with sentences that require simple lexical inferences"

Connected through hyperedge HE-8 (induced by source paper "an embarrassingly simple approach for transfer learning from pretrained language models"):
  with members:
    - "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"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"

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)