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: "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
  Abstract: In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank in English/Spanish respectively.

Connected through hyperedge HE-1 (induced by source paper "an external knowledge enhanced multi label charge prediction approach with label number learning"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "self attention with relative position representations"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "empirical study of deep learning for text classification in legal document review"
    - "sgm sequence generation model for multi label classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-2 (induced by source paper "gated recurrent neural network approach for multilabel emotion detection in microblogs"):
  with members:
    - "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"
    - "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 "caire_hkust at semeval 2019 task 3 hierarchical attention for dialogue emotion classification"):
  with members:
    - "universal transformers"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "deep contextualized word representations"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "emotionx hsu adopting pre trained bert for emotion classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "recurrent neural network for text classification with multi task learning"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "emotionx dlc self attentive bilstm for detecting sequential emotions in dialogue"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a structured self attentive sentence embedding"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)