human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "reconhecimento de actos de dialogo hierarquicos e multi etiqueta em dados em espanhol"):
  Abstract: EnglishDialog acts reveal the intention behind the uttered words. Thus, their automatic recognition is important for a dialog system trying to understand its conversational partner. The study presented in this article approaches that task on the DIHANA corpus, whose three-level dialog act annotation scheme poses problems which have not been explored in recent studies. In addition to the hierarchical problem, the two lower levels pose multi-label classification problems. Furthermore, each level in the hierarchy refers to a different aspect concerning the intention of the speaker both in terms of the structure of the dialog and the task. Also, since its dialogs are in Spanish, it allows us to assess whether the state-of-the-art approaches on English data generalize to a different language. More specifically, we compare the performance of different segment representation approaches focusing on both sequences and patterns of words and assess the importance of the dialog history and the relations between the multiple levels of the hierarchy. Concerning the single-label classification problem posed by the top level, we show that the conclusions drawn on English data also hold on Spanish data. Furthermore, we show that the approaches can be adapted to multi-label scenarios. Finally, by hierarchically combining the best classifiers for each level, we achieve the best results reported for this corpus. portuguesOs actos de dialogo revelam a intencao por tras das palavras pronunciadas. Por isso, o seu reconhecimento automatico e importante para um sistema de dialogo que tenta entender o seu interlocutor. O estudo apresentado neste artigo aborda essa tarefa no corpus DIHANA, cujo esquema de anotacao de actos de dialogo em tres niveis coloca problemas que nao foram explorados em estudos recentes. Alem do problema hierarquico, os dois niveis inferiores colocam problemas de classificacao multi-etiqueta. Alem disso, cada nivel da hierarquia refere-se a um aspecto diferente relativo a intencao do orador, tanto em termos da estrutura do dialogo, como da tarefa. Por outro lado, uma vez que os dialogos sao em espanhol, este corpus permite-nos avaliar se as melhores abordagens para dados em ingles generalizam para uma lingua diferente. Mais especificamente, comparamos o desempenho de diferentes abordagens de representacao de segmentos, com foco tanto em sequencias como em padroes de palavras, e avaliamos a importância do historico do dialogo e das relacoes entre os multiplos niveis da hierarquia. No que diz respeito ao problema de classificacao de etiqueta unica colocado pelo nivel superior, mostramos que as conclusoes obtidas a partir de dados em ingles se mantem em dados em espanhol. Para alem disso, mostramos que as abordagens podem ser adaptadas para cenarios multi-etiqueta. Por fim, combinando hierarquicamente os melhores classificadores para cada nivel, obtemos os melhores resultados reportados para este corpus.

Includes members:
  Member "a study on dialog act recognition using character level tokenization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep dialog act recognition using multiple token segment and context information representations")
  Member "very deep convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "description based text classification with reinforcement learning")
  Member "mapping the dialog act annotations of the lego corpus into the communicative functions of iso 24617 2":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "general purpose communicative function recognition using a hierarchical network with cascading outputs and maximum a posteriori path estimation")
  Member "a latent variable recurrent neural network for discourse relation language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modelling latent skills for multitask language generation")
  Member "sequential short text classification with recurrent and convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition")
  Member "the influence of context on dialogue act recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "context aware neural based dialog act classification on automatically generated transcriptions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)