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: "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
  Abstract: This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent-units (GRU) with a convolution neural network (CNN) attention mechanism and training stacking-based ensembles for each of the sub-tasks. Our algorithm reached 3rd and 1st places in the valence ordinal classification sub-tasks in English and Spanish, respectively.

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"
    - "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 "gated recurrent neural network approach for multilabel emotion detection in microblogs"):
  with members:
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "image captioning with semantic attention"
    - "character level convolutional networks for text classification"
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "stacked attention networks for image question answering"

Connected through hyperedge HE-3 (induced by source paper "evomsa a multilingual evolutionary approach for sentiment analysis"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "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"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "niletmrg at semeval 2017 task 4 arabic sentiment analysis"
    - "are word embedding based features useful for sarcasm detection"

Connected through hyperedge HE-4 (induced by source paper "amobee at iest 2018 transfer learning from language models"):
  with members:
    - "universal sentence encoder"
    - "iest wassa 2018 implicit emotions shared task"
    - "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-5 (induced by source paper "iest wassa 2018 implicit emotions shared task"):
  with members:
    - "iiidyt at iest 2018 implicit emotion classification with deep contextualized word representations"
    - "amobee at iest 2018 transfer learning from language models"
    - "wassa 2017 shared task on emotion intensity"
    - "stance and sentiment in tweets"

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)