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 emoint 2017 tweet emotion intensity estimator"
  Abstract: The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.

Connected through hyperedge HE-1 (induced by source paper "gwu nlp lab at semeval 2019 task 3 emocontext effective contextual information in models for emotion detection in sentence level in a multigenre corpus"):
  with members:
    - "deep contextualized word representations"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "a sentiment and semantics based approach for emotion detection in textual conversations"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "affect in tweets using experts model"):
  with members:
    - "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"
    - "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 "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "word affect intensities"
    - "wassa 2017 shared task on emotion intensity"
    - "skip thought vectors"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-4 (induced by source paper "agree to disagree improving disagreement detection with dual grus"):
  with members:
    - "word affect intensities"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "bilateral multi perspective matching for natural language sentences"
    - "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
    - "stance and sentiment in tweets"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a large annotated corpus for learning natural language inference"

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)