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: "reusing neural speech representations for auditory emotion recognition"
  Abstract: Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show ~10% relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained end-to-end for emotion recognition.

Connected through hyperedge HE-1 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"
    - "multi attention recurrent network for human communication comprehension"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"

Connected through hyperedge HE-2 (induced by source paper "multimodal language analysis with recurrent multistage fusion"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "learning factorized multimodal representations"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"):
  with members:
    - "progressive neural networks for transfer learning in emotion recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "improved regularization techniques for end to end speech recognition"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "deep visual foresight for planning robot motion"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "recurrent batch normalization"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-4 (induced by source paper "multi modal emotion recognition on iemocap dataset using deep learning"):
  with members:
    - "emotion recognition from speech with recurrent neural networks"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "emotion recognition from speech with recurrent neural networks"):
  with members:
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)