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 "towards interpretable polyphonic transcription with invertible neural networks"):
  Abstract: We explore a novel way of conceptualising the task of polyphonic music transcription, using so-called invertible neural networks. Invertible models unify both discriminative and generative aspects in one function, sharing one set of parameters. Introducing invertibility enables the practitioner to directly inspect what the discriminative model has learned, and exactly determine which inputs lead to which outputs. For the task of transcribing polyphonic audio into symbolic form, these models may be especially useful as they allow us to observe, for instance, to what extent the concept of single notes could be learned from a corpus of polyphonic music alone (which has been identified as a serious problem in recent research). This is an entirely new approach to audio transcription, which first of all necessitates some groundwork. In this paper, we begin by looking at the simplest possible invertible transcription model, and then thoroughly investigate its properties. Finally, we will take first steps towards a more sophisticated and capable version. We use the task of piano transcription, and specifically the MAPS dataset, as a basis for these investigations.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "model agnostic supervised local explanations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "excessive invariance causes adversarial vulnerability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "out of distribution generalization via risk extrapolation rex")
  Member "i revnet deep invertible networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "generative modeling using the sliced wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "an experimental analysis of the entanglement problem in neural network based music transcription systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a holistic approach to polyphonic music transcription with neural networks")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "onsets and frames dual objective piano transcription":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "music2dance dancenet for music driven dance generation")

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)