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 recognition via crf attentive structured network"
  Abstract: Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.

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"
    - "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"

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"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "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:
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "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 "masking orchestration multi task pretraining for multi role dialogue representation learning"):
  with members:
    - "pretraining methods for dialog context representation learning"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "language model pre training for hierarchical document representations"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "attention is all you need"
    - "dialogue act sequence labeling using hierarchical encoder with crf"

Connected through hyperedge HE-5 (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"
    - "a dual attention hierarchical recurrent neural network for dialogue act classification"
    - "improved dynamic memory network for dialogue act classification with adversarial training"
    - "deep contextualized word representations"
    - "neural based context representation learning for dialog act classification"
    - "attention is all you need"
    - "dialogue act sequence labeling using hierarchical encoder with crf"

Connected through hyperedge HE-6 (induced by source paper "filling conversation ellipsis for better social dialog understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "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"
    - "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-7 (induced by source paper "improved dynamic memory network for dialogue act classification with adversarial training"):
  with members:
    - "improving automatic source code summarization via deep reinforcement learning"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "data noising as smoothing in neural network language models"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "image captioning with semantic attention"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-8 (induced by source paper "a dual attention hierarchical recurrent neural network for dialogue act classification"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "don t get lost in negation an effective negation handled dialogue acts prediction algorithm for twitter customer service conversations"
    - "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)