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 sequence labeling using hierarchical encoder with crf"
  Abstract: Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to utterances in a conversation. The problem of associating semantic labels to utterances can be treated as a sequence labeling problem. In this work, we build a hierarchical recurrent neural network using bidirectional LSTM as a base unit and the conditional random field (CRF) as the top layer to classify each utterance into its corresponding dialogue act. The hierarchical network learns representations at multiple levels, i.e., word level, utterance level, and conversation level. The conversation level representations are input to the CRF layer, which takes into account not only all previous utterances but also their dialogue acts, thus modeling the dependency among both, labels and utterances, an important consideration of natural dialogue. We validate our approach on two different benchmark data sets, Switchboard and Meeting Recorder Dialogue Act, and show performance improvement over the state-of-the-art methods by $2.2\%$ and $4.1\%$ absolute points, respectively. It is worth noting that the inter-annotator agreement on Switchboard data set is $84\%$, and our method is able to achieve the accuracy of about $79\%$ despite being trained on the noisy data.

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"
    - "dialogue act recognition via crf attentive structured network"
    - "decoupled weight decay regularization"
    - "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"
    - "dialogue act recognition via crf attentive structured network"
    - "decoupled weight decay regularization"
    - "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"
    - "dialogue act recognition via crf attentive structured network"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning"):
  with members:
    - "multi task learning for continuous control"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "global locally self attentive dialogue state tracker"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "learning personalized end to end goal oriented dialog"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"

Connected through hyperedge HE-6 (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"
    - "dialogue act recognition via crf attentive structured network"
    - "neural based context representation learning for dialog act classification"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "short text conversation based on deep neural network and analysis on evaluation measures"):
  with members:
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "know what you don t know unanswerable questions for squad"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-8 (induced by source paper "emotionx ku bert max based contextual emotion classifier"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"

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)