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 "interpreting and explaining deep neural networks for classification of audio signals"):
  Abstract: Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. This paper explores the interpretability of neural networks in the audio domain by using the previously proposed technique of layer-wise relevance propagation (LRP). We present a novel audio dataset of English spoken digits which we use for classification tasks on spoken digits and speaker's gender. We use LRP to identify relevant features for two neural network architectures that process either waveform or spectrogram representations of the data. Based on the relevance scores obtained from LRP, hypotheses about the neural networks' feature selection are derived and subsequently tested through systematic manipulations of the input data. The results confirm that the networks are highly reliant on features marked as relevant by LRP.

Includes members:
  Member "unmasking clever hans predictors and assessing what machines really learn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding image captioning models beyond visualizing attention")
  Member "explaining recurrent neural network predictions in sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "explaining nonlinear classification decisions with deep taylor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "interpretable explanations of black boxes by meaningful perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "very deep convolutional neural networks for raw waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal dense video captioning")
  Member "not just a black box learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "visualizing and understanding neural models in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")

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)