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: "the influence of context on dialogue act recognition"
  Abstract: This article presents a deep analysis of the influence of context information on dialogue act recognition. We performed experiments on the annotated subsets of three different corpora: the widely explored Switchboard Dialog Act Corpus, as well as the unexplored LEGO and Cambridge Restaurant corpora. #R##N#In contrast with previous work, especially in what concerns the Switchboard corpus, we used an event-based classification approach, using SVMs, instead of the more common sequential approaches, such as HMMs. We opted for such an approach so that we could control the amount of provided context information and, thus, explore its range of influence. Our base features consist of n-grams, punctuation, and wh-words. Context information is obtained from previous utterances and provided in three ways -- n-grams, n-grams tagged with relative position, and dialogue act classifications. A comparative study was conducted to evaluate the performance of the three approaches. From it, we were able to assess the importance of context information on dialogue act recognition, as well as its range of influence for each of the three selected representations. #R##N#In addition to the conclusions originated by the analysis, this work also produced results that advance the state-of-the-art, especially considering previous work on the Switchboard corpus. Furthermore, since, to our knowledge, the remaining datasets had not been previously explored for this task, our experiments can be used as baselines for future work on those corpora.

Connected through hyperedge HE-1 (induced by source paper "context aware neural based dialog act classification on automatically generated transcriptions"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "neural based context representation learning for dialog act classification"
    - "the microsoft 2017 conversational speech recognition system"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "dialogue act classification in group chats with dag lstms"):
  with members:
    - "tree to tree neural networks for program translation"
    - "deep dialog act recognition using multiple token segment and context information representations"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "dag based long short term memory for neural word segmentation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "enhanced lstm for natural language inference"
    - "exploiting sentence and context representations in deep neural models for spoken language understanding"

Connected through hyperedge HE-3 (induced by source paper "reconhecimento de actos de dialogo hierarquicos e multi etiqueta em dados em espanhol"):
  with members:
    - "a study on dialog act recognition using character level tokenization"
    - "very deep convolutional networks for text classification"
    - "mapping the dialog act annotations of the lego corpus into the communicative functions of iso 24617 2"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "a study on dialog act recognition using character level tokenization"):
  with members:
    - "hierarchical character word models for language identification"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "character level convolutional networks for text classification"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-5 (induced by source paper "learning discourse level diversity for neural dialog models using conditional variational autoencoders"):
  with members:
    - "a persona based neural conversation model"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to sequence learning as beam search optimization"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-6 (induced by source paper "dialogue act sequence labeling using hierarchical encoder with crf"):
  with members:
    - "learning to attend copy and generate for session based query suggestion"
    - "a deep reinforced model for abstractive summarization"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "incorporating loose structured knowledge into conversation modeling via recall gate lstm"):
  with members:
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "end to end memory networks"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-8 (induced by source paper "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"):
  with members:
    - "recurrent neural network encoder with attention for community question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "neural machine translation by jointly learning to align and translate"
    - "a new approach to keyphrase extraction using 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)