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: "attention augmented end to end multi task learning for emotion prediction from speech"
  Abstract: Despite the increasing research interest in end-to-end learning systems for speech emotion recognition, conventional systems either suffer from the overfitting due in part to the limited training data, or do not explicitly consider the different contributions of automatically learnt representations for a specific task. In this contribution, we propose a novel end-to-end framework which is enhanced by learning other auxiliary tasks and an attention mechanism. That is, we jointly train an end-to-end network with several different but related emotion prediction tasks, i.e., arousal, valence, and dominance predictions, to extract more robust representations shared among various tasks than traditional systems with the hope that it is able to relieve the overfitting problem. Meanwhile, an attention layer is implemented on top of the layers for each task, with the aim to capture the contribution distribution of different segment parts for each individual task. To evaluate the effectiveness of the proposed system, we conducted a set of experiments on the widely used database IEMOCAP. The empirical results show that the proposed systems significantly outperform corresponding baseline systems.

Connected through hyperedge HE-1 (induced by source paper "similarity of speech emotion in different languages revealed by a neural network with attention"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "one shot learning with memory augmented neural networks"
    - "very deep convolutional networks for text classification"
    - "key value memory networks for directly reading documents"
    - "end to end memory networks"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "direct modelling of speech emotion from raw speech"
    - "automated screening for distress a perspective for the future"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "flipped adversarial autoencoders"
    - "transfer learning for improving speech emotion classification accuracy"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "emotion recognition in speech using cross modal transfer in the wild"

Connected through hyperedge HE-3 (induced by source paper "speech sentiment analysis via pre trained features from end to end asr models"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

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)