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 "multi task semi supervised adversarial autoencoding for speech emotion"):
  Abstract: Despite the widespread use of supervised deep learning methods for affect recognition from speech, they are severely limited by the lack of a sufficient amount of labelled speech data. Considering the abundant availability of unlabelled data, this paper proposed a semi-supervised model that can effectively utilise the unlabelled data in multi-task learning way in order to improve the performance of speech emotion recognition. The proposed model adversarialy learns a shared representation for two auxiliary tasks along with emotion identification as the main task. We consider speaker and gender identification as auxiliary tasks in order to operate the model on any large audio corpus. We demonstrate that in a scenario with limited labelled training samples, one can significantly improve the performance of a supervised classification task by simultaneously training with additional auxiliary tasks having an availability of large amount of data. The proposed model is rigorously evaluated for both categorical and dimensional emotion classification tasks. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on two publicly available datasets.

Includes members:
  Member "exploiting unlabeled data in cnns by self supervised learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "direct modelling of speech emotion from raw speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "automated screening for distress a perspective for the future":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "attention augmented end to end multi task learning for emotion prediction from speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "similarity of speech emotion in different languages revealed by a neural network with attention")
  Member "phonocardiographic sensing using deep learning for abnormal heartbeat detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "flipped adversarial autoencoders":
    also connected through other hyperedges:
      (none)
  Member "transfer learning for improving speech emotion classification accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "leveraging unlabeled data for crowd counting by learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)