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: "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
  Abstract: We introduce Kapre, Keras layers for audio and music signal preprocessing. Music research using deep neural networks requires a heavy and tedious preprocessing stage, for which audio processing parameters are often ignored in parameter optimisation. To solve this problem, Kapre implements time-frequency conversions, normalisation, and data augmentation as Keras layers. We report simple benchmark results, showing real-time on-GPU preprocessing adds a reasonable amount of computation.

Connected through hyperedge HE-1 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "energy and policy considerations for deep learning in nlp"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-2 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "end to end music source separation is it possible in the waveform domain"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "a tutorial on deep learning for music information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "nnaudio an on the fly gpu audio to spectrogram conversion toolbox using 1d convolution neural networks"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "towards interpretable polyphonic transcription with invertible neural networks"
    - "an attention mechanism for musical instrument recognition"
    - "zero shot learning for audio based music classification and tagging"
    - "learning soft attention models for tempo invariant audio sheet music retrieval"
    - "a bi directional transformer for musical chord recognition"
    - "cover detection using dominant melody embeddings"
    - "end to end learning for music audio tagging at scale"

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"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "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 "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-6 (induced by source paper "a tutorial on deep learning for music information retrieval"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "a neural parametric singing synthesizer"
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-7 (induced by source paper "transfer learning for music classification and regression tasks"):
  with members:
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "look listen and learn"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "explaining deep convolutional neural networks on music classification"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "automatic tagging using deep convolutional neural networks"
    - "soundnet learning sound representations from unlabeled video"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-8 (induced by source paper "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "understanding how image quality affects deep neural networks"
    - "automatic tagging using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "theano new features and speed improvements"

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)