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: "convolutional attention networks for multimodal emotion recognition from speech and text data"
  Abstract: Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.

Connected through hyperedge HE-1 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "end to end music source separation is it possible in the waveform domain"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "a tutorial on deep learning for music information retrieval"
    - "attention is all you need"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

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"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal speech emotion recognition using audio and text"
    - "frage frequency agnostic word representation"

Connected through hyperedge HE-3 (induced by source paper "strong and simple baselines for multimodal utterance embeddings"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning factorized multimodal representations"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-4 (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"
    - "multimodal speech emotion recognition using audio and text"
    - "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-5 (induced by source paper "complementary fusion of multi features and multi modalities in sentiment analysis"):
  with members:
    - "multimodal speech emotion recognition using audio and text"
    - "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"

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)