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 "learning to model aspects of hearing perception using neural loss functions"):
  Abstract: We present a framework to model the perceived quality of audio signals by combining convolutional architectures, with ideas from classical signal processing, and describe an approach to enhancing perceived acoustical quality. We demonstrate the approach by transforming the sound of an inexpensive musical with degraded sound quality to that of a high-quality musical instrument without the need for parallel data which is often hard to collect. We adapt the classical approach of a simple adaptive EQ filtering to the objective criterion learned by a neural architecture and optimize it to get the signal of our interest. Since we learn adaptive masks depending on the signal of interest as opposed to a fixed transformation for all the inputs, we show that shallow neural architectures can achieve the desired result. A simple constraint on the objective and the initialization helps us in avoiding adversarial examples, which otherwise would have produced noisy, unintelligible audio. We believe that the current framework proposed has enormous applications, in a variety of problems where one can learn a loss function depending on the problem, using a neural architecture and optimize it after it has been learned.

Includes members:
  Member "end to end spoken language translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space")
  Member "audio linguistic embeddings for spoken sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "neural style transfer for audio spectograms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "conditional end to end audio transforms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space")
  Member "quality net an end to end non intrusive speech quality assessment model based on blstm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "learned perceptual image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "super resolving commercial satellite imagery using realistic training data")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised neural architecture search")

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)