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: "raw multi channel audio source separation using multi resolution convolutional auto encoders"
  Abstract: Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing.

Connected through hyperedge HE-1 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"

Connected through hyperedge HE-2 (induced by source paper "multichannel speech enhancement by raw waveform mapping using fully convolutional networks"):
  with members:
    - "dilated fcn listening longer to hear better"
    - "adversarial audio synthesis"
    - "speech dereverberation based on integrated deep and ensemble learning algorithm"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "multi resolution fully convolutional neural networks for monaural audio source separation"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-4 (induced by source paper "audio source separation via multi scale learning with dilated dense u nets"):
  with members:
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "deep image prior"
    - "segan speech enhancement generative adversarial network"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "densely connected convolutional networks"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "end to end music source separation is it possible in the waveform domain"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "randomly weighted cnns for music audio classification"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "end to end source separation with adaptive front ends"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "does phase matter for monaural source separation"
    - "end to end learning for music audio tagging at scale"
    - "a wavenet for speech denoising"

Connected through hyperedge HE-6 (induced by source paper "wave u net a multi scale neural network for end to end audio source separation"):
  with members:
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"

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)