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 "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition"):
  Abstract: Cross-lingual speech emotion recognition (SER) is a crucial task for many real-world applications. The performance of SER systems is often degraded by the differences in the distributions of training and test data. These differences become more apparent when training and test data belong to different languages, which cause a significant performance gap between the validation and test scores. It is imperative to build more robust models that can fit in practical applications of SER systems. Therefore, in this paper, we propose a Generative Adversarial Network (GAN)-based model for multilingual SER. Our choice of using GAN is motivated by their great success in learning the underlying data distribution. The proposed model is designed in such a way that can learn language invariant representations without requiring target-language data labels. We evaluate our proposed model on four different language emotional datasets, including an Urdu-language dataset to also incorporate alternative languages for which labelled data is difficult to find and which have not been studied much by the mainstream community. Our results show that our proposed model can significantly improve the baseline cross-lingual SER performance for all the considered datasets including the non-mainstream Urdu language data without requiring any labels.

Includes members:
  Member "direct modelling of speech emotion from raw speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning")
  Member "automated screening for distress a perspective for the future":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning")
  Member "cross lingual and multilingual speech emotion recognition on english and french":
    also connected through other hyperedges:
      (none)
  Member "transferable positive negative speech emotion recognition via class wise adversarial domain adaptation":
    also connected through other hyperedges:
      (none)
  Member "phonocardiographic sensing using deep learning for abnormal heartbeat detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "automating motion correction in multishot mri using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)