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: "dialogue act classification with context aware self attention"
  Abstract: Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.

Connected through hyperedge HE-1 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition"):
  with members:
    - "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"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "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:
    - "a dual attention hierarchical recurrent neural network for dialogue act classification"
    - "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-3 (induced by source paper "message passing attention networks for document understanding"):
  with members:
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph convolutional networks for text classification"
    - "how powerful are graph neural networks"
    - "kernel graph convolutional neural networks"
    - "neural message passing for quantum chemistry"
    - "a structured self attentive sentence embedding"
    - "graph classification with 2d convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "bidirectional context aware hierarchical attention network for document understanding"):
  with members:
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "context aware self attention networks"
    - "deep communicating agents for abstractive summarization"
    - "improving the transformer translation model with document level context"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "context aware neural machine translation learns anaphora resolution"
    - "three factors influencing minima in sgd"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "filling conversation ellipsis for better social dialog understanding"):
  with members:
    - "midas a dialog act annotation scheme for open domain human machine spoken conversations"
    - "improving multi turn dialogue modelling with utterance rewriter"
    - "sentences with gapping parsing and reconstructing elided predicates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialogue act recognition via crf attentive structured network"
    - "opennmt open source toolkit for neural machine translation"
    - "deep semantic role labeling with self attention"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "a dual attention hierarchical recurrent neural network for dialogue act classification"):
  with members:
    - "don t get lost in negation an effective negation handled dialogue acts prediction algorithm for twitter customer service conversations"
    - "dialogue act recognition via crf attentive structured network"
    - "joint modeling of content and discourse relations in dialogues"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "an overview of multi task learning in deep neural networks"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "end to end sequence labeling via bi directional lstm cnns 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)