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 "randomly weighted cnns for music audio classification"):
  Abstract: The computer vision literature shows that randomly weighted neural networks perform reasonably as feature extractors. Following this idea, we study how non-trained (randomly weighted) convolutional neural networks perform as feature extractors for (music) audio classification tasks. We use features extracted from the embeddings of deep architectures as input to a classifier - with the goal to compare classification accuracies when using different randomly weighted architectures. By following this methodology, we run a comprehensive evaluation of the current deep architectures for audio classification, and provide evidence that the architectures alone are an important piece for resolving (music) audio problems using deep neural networks.

Includes members:
  Member "one deep music representation to rule them all a comparative analysis of different representation learning strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative autoregressive networks for 3d dancing move synthesis from music")
  Member "local explanation methods for deep neural networks lack sensitivity to parameter values":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tedl a text encryption method based on deep learning")
  Member "intriguing properties of randomly weighted networks generalizing while learning next to nothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic pyramid for image generation")
  Member "dlr toward a deep learned rhythmic representation for music content analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bottom up broadcast neural network for music genre classification")
  Member "look listen and learn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "deep convolutional neural networks and data augmentation for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "multi label music genre classification from audio text and images using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset")

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)