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: "end to end multimodal emotion recognition using deep neural networks"
  Abstract: Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human–computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a convolutional neural network (CNN) to extract features from the speech, while for the visual modality a deep residual network of 50 layers is used. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, long short-term memory networks are utilized. The system is then trained in an end-to-end fashion where—by also taking advantage of the correlations of each of the streams—we manage to significantly outperform, in terms of concordance correlation coefficient, traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

Connected through hyperedge HE-1 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-2 (induced by source paper "multi task learning network for emotion recognition in conversation"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "deep contextualized word representations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "continuous emotion recognition via deep convolutional autoencoder and support vector regressor"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"
    - "emotional expression classification using time series kernels"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-4 (induced by source paper "deep learning for human affect recognition insights and new developments"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "regularization for deep learning a taxonomy"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "attention is all you need"
    - "understanding hidden memories of recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "learning audio sequence representations for acoustic event classification"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-6 (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"
    - "multimodal speech emotion recognition using audio and text"

Connected through hyperedge HE-7 (induced by source paper "deep emotion facial expression recognition using attentional convolutional network"):
  with members:
    - "spatial temporal recurrent neural network for emotion recognition"
    - "ad net audio visual convolutional neural network for advertisement detection in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "an experimental study of deep convolutional features for iris recognition"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "mask r cnn"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-8 (induced by source paper "jointly aligning and predicting continuous emotion annotations"):
  with members:
    - "trainable time warping aligning time series in the continuous time domain"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"

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.CV(Computer Vision and Pattern Recognition)