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: "transfer learning for improving speech emotion classification accuracy"
  Abstract: The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that is novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and SVM baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.

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"
    - "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"
    - "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"
    - "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 "automated screening for distress a perspective for the future"):
  with members:
    - "depression scale recognition from audio visual and text analysis"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "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-5 (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"
    - "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"

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)