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: "jointly detecting and separating singing voice a multi task approach"
  Abstract: A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns to solve related auxiliary tasks on additional datasets to exploit their correlation. While intuitive in principle, it can be challenging to identify related tasks and construct the model to optimally share information between tasks. In this paper, we explore vocal activity detection as an additional task to stabilise and improve the performance of vocal separation. Further, we identify problematic biases specific to each dataset that could limit the generalisation capability of separation and detection models, to which our proposed approach is robust. Experiments show improved performance in separation as well as vocal detection compared to single-task baselines. However, we find that the commonly used Signal-to-Distortion Ratio (SDR) metrics did not capture the improvement on non-vocal sections, indicating the need for improved evaluation methodologies.

Connected through hyperedge HE-1 (induced by source paper "interleaved multitask learning for audio source separation with independent databases"):
  with members:
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "music source separation using stacked hourglass networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "multi scale multi band densenets for audio source separation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "a functional taxonomy of music generation systems"
    - "svsgan singing voice separation via generative adversarial network"
    - "exploiting saliency for object segmentation from image level labels"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "densely connected convolutional networks"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "joint optimization of masks and deep recurrent neural networks for monaural 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)