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: "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
  Abstract: Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken into account. We propose an utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one. In our setup, the BiRNN is given the input set of current and preceding utterances. Our model outperforms previous models that use only preceding utterances as context on the used corpus. Another contribution of the article is to discover the amount of information in each utterance to classify the subsequent one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification. We use character- and word-level features to represent the utterances. The results are presented for character and word feature representations and as an ensemble model of both representations. We found that when classifying short utterances, the closest preceding utterances contributes to a higher degree.

Connected through hyperedge HE-1 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators"):
  with members:
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "emotionlines an emotion corpus of multi party conversations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-2 (induced by source paper "persuasion for good towards a personalized persuasive dialogue system for social good"):
  with members:
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "sentiment adaptive end to end dialog systems"
    - "learning to generate reviews and discovering sentiment"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "enriching word vectors with subword information"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"

Connected through hyperedge HE-3 (induced by source paper "multi scale time frequency attention for acoustic event detection"):
  with members:
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "learning how to listen a temporal frequential attention model for sound event detection"
    - "a simple model for detection of rare sound events"
    - "residual attention network for image classification"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "multi scale time frequency attention for rare sound event detection"):
  with members:
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "learning how to listen a temporal frequential attention model for sound event detection"
    - "a simple model for detection of rare sound events"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "enriching existing conversational emotion datasets with dialogue acts using neural annotators"):
  with members:
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "emotionlines an emotion corpus of multi party conversations"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-6 (induced by source paper "discourse wizard discovering deep discourse structure in your conversation with rnns"):
  with members:
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "tensorflow a system for large scale machine learning"
    - "recurrent convolutional neural networks for discourse compositionality"

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)