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: "deep convolutional neural network with mixup for environmental sound classification"
  Abstract: Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noise-like nature and may be produced by a wide variety of sources. In this paper, we propose to use a novel deep convolutional neural network for ESC tasks. Our network architecture uses stacked convolutional and pooling layers to extract high-level feature representations from spectrogram-like features. Furthermore, we apply mixup to ESC tasks and explore its impacts on classification performance and feature distribution. Experiments were conducted on UrbanSound8K, ESC-50 and ESC-10 datasets. Our experimental results demonstrated that our ESC system has achieved the state-of-the-art performance (83.7%) on UrbanSound8K and competitive performance on ESC-50 and ESC-10.

Connected through hyperedge HE-1 (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"
    - "multi stream network with temporal attention for environmental sound classification"
    - "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"
    - "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-2 (induced by source paper "attention based convolutional recurrent neural network for environmental sound classification"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "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-3 (induced by source paper "multi stream network with temporal attention for environmental sound classification"):
  with members:
    - "learning environmental sounds with multi scale convolutional neural network"
    - "a simple model for detection of rare sound events"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "temporal attention model for neural machine translation"
    - "soundnet learning sound representations from unlabeled video"

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)