human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "direct modelling of speech emotion from raw speech"):
  Abstract: Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained traction in this field for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers in the feature extraction block that are jointly trained with the LSTM based classification network for emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN with hand-engineered features on IEMOCAP and MSP-IMPROV datasets.

Includes members:
  Member "automated screening for distress a perspective for the future":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "phonocardiographic sensing using deep learning for abnormal heartbeat detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "cross corpus speech emotion classification an effective transfer learning technique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "cnn lstm architecture for speech emotion recognition with data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "variational autoencoders for learning latent representations of speech emotion a preliminary study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "very deep convolutional neural networks for raw waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text classification with lexicon from preattention mechanism")

Question: Please tell me which class the source paper 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.SD(Sound)