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: "structured dropout for weak label and multi instance learning and its application to score informed source separation"
  Abstract: Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music.

Connected through hyperedge HE-1 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"

Connected through hyperedge HE-2 (induced by source paper "weak label supervision for monaural source separation using non negative denoising variational autoencoders"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "generative adversarial source separation"
    - "learning latent representations for speech generation and transformation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "a neural network alternative to non negative audio models"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-3 (induced by source paper "jointly detecting and separating singing voice a multi task approach"):
  with members:
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

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.LG(Machine Learning)