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: "a context based approach for dialogue act recognition using simple recurrent neural networks"
  Abstract: Dialogue act recognition is an important part of natural language understanding. We investigate the way dialogue act corpora are annotated and the learning approaches used so far. We find that the dialogue act is context-sensitive within the conversation for most of the classes. Nevertheless, previous models of dialogue act classification work on the utterance-level and only very few consider context. We propose a novel context-based learning method to classify dialogue acts using a character-level language model utterance representation, and we notice significant improvement. We evaluate this method on the Switchboard Dialogue Act corpus, and our results show that the consideration of the preceding utterances as a context of the current utterance improves dialogue act detection.

Connected through hyperedge HE-1 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "self attention with relative position representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a deep sequential model for discourse parsing on multi party dialogues"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialogue act recognition via crf attentive structured network"

Connected through hyperedge HE-2 (induced by source paper "guider l attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "a dual attention hierarchical recurrent neural network for dialogue act classification"
    - "dialogue act recognition via crf attentive structured network"
    - "decoupled weight decay regularization"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "sequence to sequence learning as beam search optimization"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "guiding attention in sequence to sequence models for dialogue act prediction"):
  with members:
    - "dialogue act recognition via crf attentive structured network"
    - "decoupled weight decay regularization"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "a persona based neural conversation model"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators"):
  with members:
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "emotionlines an emotion corpus of multi party conversations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-5 (induced by source paper "enriching existing conversational emotion datasets with dialogue acts using neural annotators"):
  with members:
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-6 (induced by source paper "discourse wizard discovering deep discourse structure in your conversation with rnns"):
  with members:
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "tensorflow a system for large scale machine learning"
    - "recurrent convolutional neural networks for discourse compositionality"

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)