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: "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"
  Abstract: The performance of an Acoustic Scene Classification (ASC) system is highly depending on the latent temporal dynamics of the audio signal. In this paper, we proposed a multiple layers temporal pooling method using CNN feature sequence as in-put, which can effectively capture the temporal dynamics for an entire audio signal with arbitrary duration by building direct connections between the sequence and its time indexes. We applied our novel framework on DCASE 2018 task 1, ASC. For evaluation, we trained a Support Vector Machine (SVM) with the proposed Multi-Layered Temporal Pooling (MLTP) learned features. Experimental results on the development dataset, usage of the MLTP features significantly improved the ASC performance. The best performance with 75.28% accuracy was achieved by using the optimal setting found in our experiments.

Connected through hyperedge HE-1 (induced by source paper "dcase 2019 cnn depth analysis with different channel inputs for acoustic scene classification"):
  with members:
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "xception deep learning with depthwise separable convolutions"
    - "soundnet learning sound representations from unlabeled video"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

Connected through hyperedge HE-2 (induced by source paper "on the performance of residual block design alternatives in convolutional neural networks for end to end audio classification"):
  with members:
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "very deep convolutional neural networks for raw waveforms"
    - "identity mappings in deep residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

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)