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: "does phase matter for monaural source separation"
  Abstract: The "cocktail party" problem of fully separating multiple sources from a single channel audio waveform remains unsolved. Current biological understanding of neural encoding suggests that phase information is preserved and utilized at every stage of the auditory pathway. However, current computational approaches primarily discard phase information in order to mask amplitude spectrograms of sound. In this paper, we seek to address whether preserving phase information in spectral representations of sound provides better results in monaural separation of vocals from a musical track by using a neurally plausible sparse generative model. Our results demonstrate that preserving phase information reduces artifacts in the separated tracks, as quantified by the signal to artifact ratio (GSAR). Furthermore, our proposed method achieves state-of-the-art performance for source separation, as quantified by a mean signal to interference ratio (GSIR) of 19.46.

Connected through hyperedge HE-1 (induced by source paper "the conversation deep audio visual speech enhancement"):
  with members:
    - "end to end audiovisual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "improved speech reconstruction from silent video"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "lipnet end to end sentence level lipreading"
    - "audio visual speech enhancement using multimodal deep convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "raw multi channel audio source separation using multi resolution convolutional auto encoders"
    - "end to end source separation with adaptive front ends"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "end to end learning for music audio tagging at scale"
    - "a wavenet for speech denoising"

Connected through hyperedge HE-3 (induced by source paper "raw multi channel audio source separation using multi resolution convolutional auto encoders"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "time frequency trade offs for audio source separation with binary masks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "improving dnn based music source separation using phase features"):
  with members:
    - "multi scale multi band densenets for audio source separation"

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)