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 "machine learning for music genre multifaceted review and experimentation with audioset"):
  Abstract: Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of what genre is. Music categorizations are vague and unclear, suffering from human subjectivity and lack of agreement. In its first part, this paper offers a survey trying to cover the many different aspects of the matter. Its main goal is give the reader an overview of the history and the current state-of-the-art, exploring techniques and datasets used to the date, as well as identifying current challenges, such as this ambiguity of genre definitions or the introduction of human-centric approaches. The paper pays special attention to new trends in machine learning applied to the music annotation problem. Finally, we also include a music genre classification experiment that compares different machine learning models using Audioset.

Includes members:
  Member "end to end speech recognition from the raw waveform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech")
  Member "learning to separate object sounds by watching unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "audio based disambiguation of music genre tags":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging knowledge bases and parallel annotations for music genre translation")
  Member "music mood detection based on audio and lyrics with deep neural net":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection")
  Member "the microsoft 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep segmental phonetic posterior grams based discovery of non categories in l2 english speech")
  Member "surrey cvssp system for dcase2017 challenge task 4":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "audio set classification with attention model a probabilistic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning multi instrument classification with partial labels")
  Member "reducing model complexity for dnn based large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "enhancing sound texture in cnn based acoustic scene classification")

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)