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: "randomly weighted cnns for music audio classification"
  Abstract: The computer vision literature shows that randomly weighted neural networks perform reasonably as feature extractors. Following this idea, we study how non-trained (randomly weighted) convolutional neural networks perform as feature extractors for (music) audio classification tasks. We use features extracted from the embeddings of deep architectures as input to a classifier - with the goal to compare classification accuracies when using different randomly weighted architectures. By following this methodology, we run a comprehensive evaluation of the current deep architectures for audio classification, and provide evidence that the architectures alone are an important piece for resolving (music) audio problems using deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "self supervised learning by cross modal audio video clustering"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "self supervised learning for video correspondence flow"
    - "learning correspondence from the cycle consistency of time"
    - "self supervised audio visual co segmentation"
    - "video representation learning by dense predictive coding"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "scaling egocentric vision the epic kitchens dataset"

Connected through hyperedge HE-2 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "end to end environmental sound classification using a 1d convolutional neural network"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "very deep convolutional neural networks for raw waveforms"
    - "cnn architectures for large scale audio classification"
    - "automatic environmental sound recognition performance versus computational cost"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-6 (induced by source paper "bottom up broadcast neural network for music genre classification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "transfer learning for music classification and regression tasks"

Connected through hyperedge HE-7 (induced by source paper "cross representation transferability of adversarial perturbations from spectrograms to audio waveforms"):
  with members:
    - "universal adversarial audio perturbations"
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "multi label music genre classification from audio text and images using deep features"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-8 (induced by source paper "training neural audio classifiers with few data"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "deep learning for audio transcription on low resource datasets"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "unsupervised learning of semantic audio representations"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"

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)