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 "deep learning for audio signal processing"):
  Abstract: Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e., audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.

Includes members:
  Member "piano genie":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring conditioning for generative music systems with human interpretable controls")
  Member "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "toward domain invariant speech recognition via large scale training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing speech recognition for the edge")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what is the state of neural network pruning")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning disentangled representations of timbre and pitch for musical instrument sounds using gaussian mixture variational autoencoders")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")

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)