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 level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
  Abstract: Music auto-tagging is often handled in a similar manner to image classification by regarding the two-dimensional audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstraction. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pretrained convolutional networks separately and aggregate them altogether giving a long audio clip. Finally, we put them into fully connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms the previous state-of-the-art methods on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Connected through hyperedge HE-1 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"):
  with members:
    - "multi stream cnn based video semantic segmentation for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "digrad multi task reinforcement learning with shared actions"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-2 (induced by source paper "zero shot learning for audio based music classification and tagging"):
  with members:
    - "multi label zero shot human action recognition via joint latent embedding"
    - "representation learning of music using artist labels"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fma a dataset for music analysis"
    - "multi label zero shot learning via concept embedding"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-3 (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"
    - "randomly weighted cnns for music audio classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-4 (induced by source paper "enhancing music features by knowledge transfer from user item log data"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "transfer learning for music classification and regression tasks"
    - "fma a dataset for music analysis"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "adam a method for stochastic optimization"
    - "fitnets hints for thin deep nets"

Connected through hyperedge HE-5 (induced by source paper "representation learning of music using artist album and track information"):
  with members:
    - "transfer learning for music classification and regression tasks"
    - "representation learning of music using artist labels"
    - "fma a dataset for music analysis"
    - "deep learning and music adversaries"

Connected through hyperedge HE-6 (induced by source paper "texture selection for automatic music genre classification"):
  with members:
    - "music genre classification using spectral analysis and sparse representation of the signals"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "transfer learning for music classification and regression tasks"
    - "the state of the art ten years after a state of the art future research in music information retrieval"

Connected through hyperedge HE-7 (induced by source paper "zero shot learning and knowledge transfer in music classification and tagging"):
  with members:
    - "zero shot learning for audio based music classification and tagging"
    - "representation learning of music using artist labels"
    - "deep learning and music adversaries"

Connected through hyperedge HE-8 (induced by source paper "fast accurate and lightweight super resolution with cascading residual network"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "memnet a persistent memory network for image restoration"
    - "accelerating the super resolution convolutional neural network"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"

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.NE(Neural and Evolutionary Computing)