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: "multi temporal resolution convolutional neural networks for acoustic scene classification"
  Abstract: In this paper we present a Deep Neural Network architecture for the task of acoustic scene classification which harnesses information from increasing temporal resolutions of Mel-Spectrogram segments. This architecture is composed of separated parallel Convolutional Neural Networks which learn spectral and temporal representations for each input resolution. The resolutions are chosen to cover fine-grained characteristics of a scene's spectral texture as well as its distribution of acoustic events. The proposed model shows a 3.56% absolute improvement of the best performing single resolution model and 12.49% of the DCASE 2017 Acoustic Scenes Classification task baseline.

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "content based video music retrieval using soft intra modal structure constraint"
    - "timbre analysis of music audio signals with convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"):
  with members:
    - "learning environmental sounds with multi scale convolutional neural network"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "very deep convolutional neural networks for raw waveforms"
    - "densely connected convolutional networks"
    - "hierarchical learning for dnn based acoustic scene classification"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"

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)