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: "end to end multi channel speech separation"
  Abstract: The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation. The primary contributions of this work include 1) an integrated waveform-in waveform-out separation system in a single neural network architecture. 2) We reformulate the traditional short time Fourier transform (STFT) and inter-channel phase difference (IPD) as a function of time-domain convolution with a special kernel. 3) We further relaxed those fixed kernels to be learnable, so that the entire architecture becomes purely data-driven and can be trained from end-to-end. We demonstrate on the WSJ0 far-field speech separation task that, with the benefit of learnable spatial features, our proposed end-to-end multi-channel model significantly improved the performance of previous end-to-end single-channel method and traditional multi-channel methods.

Connected through hyperedge HE-1 (induced by source paper "channel attention dense u net for multichannel speech enhancement"):
  with members:
    - "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "supervised speech separation based on deep learning an overview"
    - "multi scale multi band densenets for audio source separation"
    - "a decomposable attention model for natural language inference"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for speech separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "single channel speech separation with auxiliary speaker embeddings"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "end to end source separation with adaptive front ends"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-3 (induced by source paper "demystifying tasnet a dissecting approach"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "wham extending speech separation to noisy environments"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "sms wsj database performance measures and baseline recipe for multi channel source separation and recognition"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "recursive speech separation for unknown number of speakers"
    - "end to end speech separation with unfolded iterative phase reconstruction"

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)