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: "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
  Abstract: The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1st position out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2% to 76.4%, thus, pushing the state-of-the-art significantly.

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"
    - "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 "affect in tweets using experts model"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "wassa 2017 shared task on emotion intensity"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "emotion intensities in tweets"
    - "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-3 (induced by source paper "towards empathetic open domain conversation models a new benchmark and dataset"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "multi task deep neural networks for natural language understanding"
    - "learning semantic textual similarity from conversations"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "training millions of personalized dialogue agents"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-4 (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"
    - "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"

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)