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: "variational autoencoders for learning latent representations of speech emotion a preliminary study"
  Abstract: Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For speech emotion recognition tasks, generating effective features is crucial. Currently, handcrafted features are mostly used for speech emotion recognition, however, features learned automatically using deep learning have shown strong success in many problems, especially in image processing. In particular, deep generative models such as Variational Autoencoders (VAEs) have gained enormous success for generating features for natural images. Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions. To the best of our knowledge, we are the first to propose VAEs for speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate that features learned by VAEs can produce state-of-the-art results for speech emotion classification.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "automated screening for distress a perspective for the future"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "cross lingual and multilingual speech emotion recognition on english and french"
    - "transferable positive negative speech emotion recognition via class wise adversarial domain adaptation"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "automating motion correction in multishot mri using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"):
  with members:
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "automating motion correction in multishot mri using generative adversarial networks"
    - "transfer learning for improving speech emotion classification accuracy"
    - "using deep autoencoders for facial expression recognition"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "generative adversarial networks and adversarial methods in biomedical image analysis"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "disentangled representation learning with information maximizing autoencoder"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "progressive growing of gans for improved quality stability and variation"
    - "colorful image colorization"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "modeling feature representations for affective speech using generative adversarial networks"):
  with members:
    - "on enhancing speech emotion recognition using generative adversarial networks"
    - "adversarial machine learning and speech emotion recognition utilizing generative adversarial networks for robustness"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "segan speech enhancement generative adversarial network"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "direct modelling of speech emotion from raw speech"):
  with members:
    - "automated screening for distress a perspective for the future"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"
    - "very deep convolutional neural networks for raw waveforms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "automated screening for distress a perspective for the future"):
  with members:
    - "transfer learning for improving speech emotion classification accuracy"
    - "depression scale recognition from audio visual and text analysis"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "gait velocity estimation using time interleaved between consecutive passive ir sensor activations"

Connected through hyperedge HE-8 (induced by source paper "adversarial machine learning and speech emotion recognition utilizing generative adversarial networks for robustness"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "transfer learning for improving speech emotion classification accuracy"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "audio adversarial examples targeted attacks on speech to text"
    - "inaudible voice commands"
    - "towards robust neural networks via random self ensemble"

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.SD(Sound)