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: "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
  Abstract: Speech emotion recognition is an important and challenging task in the realm of human-computer interaction. Prior work proposed a variety of models and feature sets for training a system. In this work, we conduct extensive experiments using an attentive convolutional neural network with multi-view learning objective function. We compare system performance using different lengths of the input signal, different types of acoustic features and different types of emotion speech (improvised/scripted). Our experimental results on the Interactive Emotional Motion Capture (IEMOCAP) database reveal that the recognition performance strongly depends on the type of speech data independent of the choice of input features. Furthermore, we achieved state-of-the-art results on the improvised speech data of IEMOCAP.

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"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning problem agnostic speech representations from multiple self supervised tasks"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a survey of recent dnn architectures on the timit phone recognition task"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning with contrastive predictive coding"
    - "towards a universal neural network encoder for time series"
    - "objects that sound"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "unsupervised learning of semantic audio representations"

Connected through hyperedge HE-4 (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"
    - "reusing neural speech representations for auditory emotion recognition"

Connected through hyperedge HE-5 (induced by source paper "towards multimodal emotion recognition in german speech events in cars using transfer learning"):
  with members:
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "learning word vectors for 157 languages"
    - "iest wassa 2018 implicit emotions shared task"
    - "amobee at iest 2018 transfer learning from language models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "emotion intensities in tweets"
    - "distant supervision for emotion detection using facebook reactions"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-6 (induced by source paper "transfer learning from sound representations for anger detection in speech"):
  with members:
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "soundnet learning sound representations from unlabeled video"
    - "yfcc100m the new data in multimedia research"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (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"
    - "multimodal speech emotion recognition using audio and text"
    - "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-8 (induced by source paper "exploring attention mechanism for acoustic based classification of speech utterances into system directed and non system directed"):
  with members:
    - "convolutional neural networks and language embeddings for end to end dialect recognition"
    - "device directed utterance detection"
    - "attention is all you need"
    - "neural machine translation by jointly learning to align and translate"

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)