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 classifying environments from the sounds they produce"
  Abstract: In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "enhancing sound texture in cnn based acoustic scene classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "reducing model complexity for dnn based large scale audio classification"
    - "a joint detection classification model for audio tagging of weakly labelled data"
    - "cnn architectures for large scale audio classification"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "acoustic scene classification by implicitly identifying distinct sound events"):
  with members:
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "a hybrid approach with multi channel i vectors and convolutional neural networks for acoustic scene classification"
    - "audio event detection using weakly labeled data"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "cross task pre training for acoustic scene classification"):
  with members:
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-5 (induced by source paper "sub spectrogram segmentation for environmental sound classification via convolutional recurrent neural network and score level fusion"):
  with members:
    - "subspectralnet using sub spectrogram based convolutional neural networks for acoustic scene classification"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-6 (induced by source paper "acoustic scene classification based on a large margin factorized cnn"):
  with members:
    - "subspectralnet using sub spectrogram based convolutional neural networks for acoustic scene classification"
    - "mixup beyond empirical risk minimization"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "factorized convolutional neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "dcase 2018 challenge surrey cross task convolutional neural network baseline"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "automatic acoustic detection of birds through deep learning the first bird audio detection challenge"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a comparison of deep learning methods for environmental sound"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "audio event detection using weakly labeled data"
    - "automatic tagging using deep convolutional neural networks"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "deep convolutional neural network with mixup for environmental sound classification"):
  with members:
    - "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"
    - "very deep convolutional neural networks for raw waveforms"
    - "soundnet learning sound representations from unlabeled video"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "anticipating visual 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)