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: "class conditional embeddings for music source separation"
  Abstract: Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods. While most musical source separation techniques learn an independent model for each instrument, we propose using a common embedding space for the time-frequency bins of all instruments in a mixture inspired by deep clustering and deep attractor networks. Additionally, an auxiliary network is used to generate parameters of a Gaussian mixture model (GMM) where the posterior distribution over GMM components in the embedding space can be used to create a mask that separates individual sources from a mixture. In addition to outperforming a mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily interpretable and can be used for query-based separation.

Connected through hyperedge HE-1 (induced by source paper "improving universal sound separation using sound classification"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "universal sound separation"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "sdr half baked or well done"
    - "speaker independent speech separation with deep attractor network"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "unsupervised learning of semantic audio representations"
    - "conditional image text embedding networks"

Connected through hyperedge HE-2 (induced by source paper "audio query based music source separation"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "end to end sound source separation conditioned on instrument labels"
    - "a style based generator architecture for generative adversarial networks"
    - "music source separation using stacked hourglass networks"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "sampling generative networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-3 (induced by source paper "bootstrapping deep music separation from primitive auditory grouping principles"):
  with members:
    - "unsupervised training of neural mask based beamforming"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "bootstrapping single channel source separation via unsupervised spatial clustering on stereo mixtures"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "end to end sound source separation conditioned on instrument labels"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "creating a musical performance dataset for multimodal music analysis challenges insights and applications"
    - "u net convolutional networks for biomedical image segmentation"

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)