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: "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
  Abstract: Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution. Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models deliver the abnormal heartbeat classification score with significant improvement. Our proposed approach using RNNs can be potentially be used for real-time abnormal heartbeat detection in the Internet of Medical Things for remote monitoring applications.

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"
    - "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-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"
    - "automating motion correction in multishot mri using generative adversarial networks"
    - "transfer learning for improving speech emotion classification accuracy"

Connected through hyperedge HE-3 (induced by source paper "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"):
  with members:
    - "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"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "pre training in deep reinforcement learning for automatic speech recognition"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "multi task semi supervised adversarial autoencoding for speech emotion"
    - "emorl continuous acoustic emotion classification using deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"
    - "the atari grand challenge dataset"

Connected through hyperedge HE-5 (induced by source paper "direct modelling of speech emotion from raw speech"):
  with members:
    - "automated screening for distress a perspective for the future"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "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-6 (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"
    - "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"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "towards robust neural networks via random self ensemble"

Connected through hyperedge HE-7 (induced by source paper "cross lingual speech emotion recognition urdu vs western languages"):
  with members:
    - "adversarial machine learning and speech emotion recognition utilizing generative adversarial networks for robustness"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "transfer learning for speech and language processing"

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.CV(Computer Vision and Pattern Recognition)