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: "attention based lstm for psychological stress detection from spoken language using distant supervision"
  Abstract: We propose a Long Short-Term Memory (LSTM) with attention mechanism to classify psychological stress from self-conducted interview transcriptions. We apply distant supervision by automatically labeling tweets based on their hash-tag content, which complements and expands the size of our corpus. This additional data is used to initialize the model parameters, and which it is fine-tuned using the interview data. This improves the model's robustness, especially by expanding the vocabulary size. The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74.1%) and f-score (74.3%). Furthermore, we show that distant supervision fine-tuning enhances the model's performance by 1.6% accuracy and 2.1% f-score. The attention mechanism helps the model to select informative words.

Connected through hyperedge HE-1 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"

Connected through hyperedge HE-2 (induced by source paper "progress notes classification and keyword extraction using attention based deep learning models with bert"):
  with members:
    - "publicly available clinical bert embeddings"
    - "deep learning for sentiment analysis a survey"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "very deep convolutional networks for text classification"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

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"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "deep contextualized word representations"
    - "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 "dreaddit a reddit dataset for stress analysis in social media"):
  with members:
    - "emo2vec learning generalized emotion representation by multi task training"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding and measuring psychological stress using social media"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "emo2vec learning generalized emotion representation by multi task training"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "tensor fusion network for multimodal sentiment analysis"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "learning language representations for typology prediction"
    - "deep multitask learning for semantic dependency parsing"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "multilingual language processing from bytes"

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)