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: "cross corpus speech emotion classification an effective transfer learning technique"
  Abstract: Cross-corpus speech emotion recognition can be a useful transfer learning technique to build a robust speech emotion recognition system by leveraging information from various speech datasets - cross-language and cross-corpus. However, more research needs to be carried out to understand the effective operating scenarios of cross-corpus speech emotion recognition, especially with the utilization of the powerful deep learning techniques. In this paper, we use five different corpora of three different languages to investigate the cross-corpus and cross-language emotion recognition using Deep Belief Networks (DBNs). Experimental results demonstrate that DBNs with generalization power offers better accuracy than a discriminative method based on Sparse Auto Encoder and SVM. Results also suggest that using a large number of languages for training and using a small fraction of target data in training can significantly boost accuracy compared to using the same language for training and testing.

Connected through hyperedge HE-1 (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-2 (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"
    - "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-3 (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"
    - "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-4 (induced by source paper "emotion recognition in speech using cross modal transfer in the wild"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "deep learning is robust to massive label noise"
    - "self supervised learning of a facial attribute embedding from video"
    - "learnable pins cross modal embeddings for person identity"
    - "exploring the limits of weakly supervised pretraining"
    - "on learning associations of faces and voices"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "look listen and learn"

Connected through hyperedge HE-5 (induced by source paper "cross lingual speech emotion recognition urdu vs western languages"):
  with members:
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "adversarial machine learning and speech emotion recognition utilizing generative adversarial networks for robustness"
    - "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)