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 dual attention hierarchical recurrent neural network for dialogue act classification"
  Abstract: Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for dialogue act classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a dialogue act and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between dialogue acts and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both dialogue acts and topics, as well as information about the interactions between them. We evaluate the performance of our model on two publicly available datasets, i.e., Switchboard and DailyDialog. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification.

Connected through hyperedge HE-1 (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 context based approach for dialogue act recognition using simple recurrent neural networks"
    - "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-2 (induced by source paper "dialogue act classification with context aware self attention"):
  with members:
    - "multi level gated recurrent neural network for dialog act classification"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "improved dynamic memory network for dialogue act classification with adversarial training"
    - "deep contextualized word representations"
    - "dialogue act recognition via crf attentive structured network"
    - "neural based context representation learning for dialog act classification"
    - "attention is all you need"
    - "dialogue act sequence labeling using hierarchical encoder with crf"

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)