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: "multimodal speech emotion recognition using audio and text"
  Abstract: Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.

Connected through hyperedge HE-1 (induced by source paper "interactive text to speech via semi supervised style transfer learning"):
  with members:
    - "visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "attention is all you need"
    - "uncovering latent style factors for expressive speech synthesis"
    - "wavenet a generative model for raw audio"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "multimodal embeddings from language models"):
  with members:
    - "multimodal abstractive summarization for how2 videos"
    - "behavior gated language models"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "m bert injecting multimodal information in the bert structure"
    - "multimodal language analysis with recurrent multistage fusion"
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "frage frequency agnostic word representation"

Connected through hyperedge HE-3 (induced by source paper "audio text sentiment analysis using deep robust complementary fusion of multi features and multi modalities"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "deep learning for sentiment analysis a survey"
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"

Connected through hyperedge HE-4 (induced by source paper "dynamic fusion for multimodal data"):
  with members:
    - "multi lingual common semantic space construction via cluster consistent word embedding"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "attentive modality hopping mechanism for speech emotion recognition"):
  with members:
    - "leveraging acoustic cues and paralinguistic embeddings to detect expression from voice"
    - "learning alignment for multimodal emotion recognition from speech"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "learning alignment for multimodal emotion recognition from speech"):
  with members:
    - "tensor fusion network for multimodal sentiment analysis"
    - "attention is all you need"
    - "attention based models for speech recognition"
    - "imagenet large scale visual recognition challenge"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "complementary fusion of multi features and multi modalities in sentiment analysis"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "multimodal speech emotion recognition and ambiguity resolution"):
  with members:
    - "efficient low rank multimodal fusion with modality specific factors"
    - "tensor fusion network for multimodal sentiment analysis"

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)