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: "spectrogram feature losses for music source separation"
  Abstract: In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve separation quality vis-a-vis a pure pixel-level loss. We show this improvement in the context of the MMDenseNet, a State-of-the-Art deep learning model for this task, for the extraction of drums and vocal sounds from songs in the musdb18 database, covering a broad range of western music genres. We believe that this finding can be generalized and applied to broader machine learning-based systems in the audio domain.

Connected through hyperedge HE-1 (induced by source paper "deep speech inpainting of time frequency masks"):
  with members:
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "towards generalized speech enhancement with generative adversarial networks"
    - "a context encoder for audio inpainting"
    - "generative image inpainting with contextual attention"
    - "supervised speech separation based on deep learning an overview"
    - "a hybrid dsp deep learning approach to real time full band speech enhancement"
    - "speech enhancement in multiple noise conditions using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "speech vgg a deep feature extractor for speech processing"):
  with members:
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "deep speech inpainting of time frequency masks"
    - "a survey on deep transfer learning"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "perceptual losses for real time style transfer and super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "deep feature consistent variational autoencoder"
    - "understanding neural networks through deep visualization"

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)