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: "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
  Abstract: The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to build a system that assigns emotion intensity values. Emotion intensity estimation is a challenging problem given the short length of the tweets, the noisy structure of the text and the lack of annotated data. To solve this problem, we developed an ensemble of two neural models, processing input on the character. and word-level with a lexicon-driven system. The correlation scores across all four emotions are averaged to determine the bottom-line competition metric, and our system ranks place forth in full intensity range and third in 0.5-1 range of intensity among 23 systems at the time of writing (June 2017).

Connected through hyperedge HE-1 (induced by source paper "a combined cnn and lstm model for arabic sentiment analysis"):
  with members:
    - "improving sentiment analysis in arabic using word representation"
    - "combining lexical features and a supervised learning approach for arabic sentiment analysis"
    - "arabic language sentiment analysis on health services"
    - "multichannel variable size convolution for sentence classification"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "enriching word vectors with subword information"
    - "feature representation in convolutional neural networks"
    - "sentiment analysis for modern standard arabic and colloquial"

Connected through hyperedge HE-2 (induced by source paper "a context based approach for dialogue act recognition using simple recurrent neural networks"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "multiplicative lstm for sequence modelling"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-3 (induced by source paper "decision support with text based emotion recognition deep learning for affective computing"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "predicting process behaviour using deep learning"
    - "fake news detection on social media a data mining perspective"
    - "how transferable are neural networks in nlp applications"
    - "detecting state of aggression in sentences using cnn"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-4 (induced by source paper "deep learning for affective computing text based emotion recognition in decision support"):
  with members:
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "predicting process behaviour using deep learning"
    - "fake news detection on social media a data mining perspective"
    - "how transferable are neural networks in nlp applications"
    - "detecting state of aggression in sentences using cnn"
    - "crowdsourcing a word emotion association lexicon"

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)