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: "polyphonic audio tagging with sequentially labelled data using crnn with learnable gated linear units"
  Abstract: Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classification (CTC) loss function on the top of Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units (GLUCTC), based on a new type of audio label data: Sequentially Labelled Data (SLD). In GLU-CTC, CTC objective function maps the frame-level probability of labels to clip-level probability of labels. To compare the mapping ability of GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling (GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we also compare the proposed GLU-CTC system with the baseline system, which is a CRNN trained using CTC loss function without GLU. The experiments show that the GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging, outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of 0.837. That means based on the same CRNN model with GLU, the performance of CTC mapping is better than the GMP and GAP mapping. Given both based on the CTC mapping, the CRNN with GLU outperforms the CRNN without GLU.

Connected through hyperedge HE-1 (induced by source paper "sound event detection with sequentially labelled data based on connectionist temporal classification and unsupervised clustering"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a report on sound event detection with different binaural features"
    - "language modeling with gated convolutional networks"
    - "audio event detection using weakly labeled data"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "duration robust sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "language modeling with gated convolutional networks"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "connectionist temporal localization for sound event detection with sequential labeling"):
  with members:
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "cnn architectures for large scale audio classification"
    - "adam a method for stochastic optimization"

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)