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: "deep scattering spectrum"
  Abstract: A scattering transform defines a locally translation invariant representation which is stable to time-warping deformation. It extends MFCC representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A frequency transposition invariant representation is obtained by applying a scattering transform along log-frequency. State-the-of-art classification results are obtained for musical genre and phone classification on GTZAN and TIMIT databases, respectively.

Connected through hyperedge HE-1 (induced by source paper "one or two components the scattering transform answers"):
  with members:
    - "joint time frequency scattering"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "look listen and learn"
    - "wavelet scattering on the pitch spiral"

Connected through hyperedge HE-2 (induced by source paper "scattering features for multimodal gait recognition"):
  with members:
    - "acoustic gait based person identification using hidden markov models"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed"):
  with members:
    - "sing symbol to instrument neural generator"
    - "end to end music source separation is it possible in the waveform domain"
    - "kymatio scattering transforms in python"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "a style based generator architecture for generative adversarial networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "introduction to voice presentation attack detection and recent advances"):
  with members:
    - "generalization of spoofing countermeasures a case study with asvspoof 2015 and btas 2016 corpora"
    - "attacking speaker recognition with deep generative models"
    - "fooling end to end speaker verification by adversarial examples"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

Connected through hyperedge HE-5 (induced by source paper "degrees of freedom analysis of unrolled neural networks"):
  with members:
    - "training deep learning based denoisers without ground truth data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled optimization with deep priors"
    - "recurrent inference machines for solving inverse problems"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "raisr rapid and accurate image super resolution"

Connected through hyperedge HE-6 (induced by source paper "trainable spectrally initializable matrix transformations in convolutional neural networks"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "squeeze and excitation networks"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "gabor frames and deep scattering networks in audio processing"):
  with members:
    - "machines listening to music the role of signal representations in learning from music"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "joint time frequency scattering"):
  with members:
    - "compressing the input for cnns with the first order scattering transform"
    - "wavelet convolutional neural networks"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "soundnet learning sound representations from unlabeled video"
    - "deep learning in neural networks"
    - "speech recognition with deep recurrent neural networks"

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)