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 stream network with temporal attention for environmental sound classification"
  Abstract: Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or front-end preprocessing, thus demonstrating better generalizability.

Connected through hyperedge HE-1 (induced by source paper "audio visual model distillation using acoustic images"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "modality distillation with multiple stream networks for action recognition"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "weakly supervised representation learning for unsynchronized audio visual events"

Connected through hyperedge HE-2 (induced by source paper "environment sound classification using multiple feature channels and attention based deep convolutional neural network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "randomly weighted cnns for music audio classification"
    - "study of residual networks for image recognition"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-3 (induced by source paper "attention based convolutional recurrent neural network for environmental sound classification"):
  with members:
    - "learning environmental sounds with multi scale convolutional neural network"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "soundnet learning sound representations from unlabeled video"
    - "acoustic scene classification classifying environments from the sounds they produce"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative and robust time frequency representations for environmental sound classification"):
  with members:
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "weakly labelled audioset tagging with attention neural networks"
    - "spatio temporal attention pooling for audio scene classification"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "raw waveform based audio classification using sample level cnn architectures"
    - "learning from between class examples for deep sound recognition"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"

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)