human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "machines listening to music the role of signal representations in learning from music"
  Abstract: Recent, extremely successful methods in deep learning, such as convolutional neural networks (CNNs) have originated in machine learning for images. When applied to music signals and related music information retrieval (MIR) problems, researchers often apply standard FFT-based signal processing methods in order to create an image from the raw audio data. The impact of this basic signal processing step on the final outcome of the MIR task has not been widely studied and is not well understood. In this contribution, we study Gabor Scattering and a new representation, namely Mel Scattering. Furthermore, we suggest an alternative enhancement of the loss function that uses transformed representations of the output data to incorporate additional available information. We show how applying various different signal analysis methods can lead to useful invariances and improve the overall performance in MIR problems by reducing the amount of necessary training data or the necessity of augmentation.

Connected through hyperedge HE-1 (induced by source paper "gabor frames and deep scattering networks in audio processing"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "joint time frequency scattering for audio classification"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)