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: "audio style transfer"
  Abstract: “Style transfer” among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose a flexible framework for the task, which uses a sound texture model to extract statistics characterizing the reference audio style, followed by an optimization-based audio texture synthesis to modify the target content. In contrast to mainstream optimization-based visual transfer method, the proposed process is initialized by the target content instead of random noise and the optimized loss is only about texture, not structure. These differences proved key for audio style transfer in our experiments. In order to extract features of interest, we investigate different architectures, whether pre-trained on other tasks, as done in image style transfer, or engineered based on the human auditory system. Experimental results on different types of audio signal confirm the potential of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges"):
  with members:
    - "neural style transfer for audio spectograms"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "neural style transfer a review"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "mask based unsupervised content transfer"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "towards content transfer through grounded text generation"
    - "weakly supervised instance segmentation using class peak response"
    - "self produced guidance for weakly supervised object localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "fast and accurate online video object segmentation via tracking parts"

Connected through hyperedge HE-3 (induced by source paper "a style transfer approach to source separation"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "unsupervised training of neural mask based beamforming"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"

Connected through hyperedge HE-4 (induced by source paper "supervised symbolic music style translation using synthetic data"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "cover song synthesis by analogy"
    - "music style transfer a position paper"
    - "symbolic music genre transfer with cyclegan"
    - "adversarially regularized autoencoders"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"

Connected through hyperedge HE-5 (induced by source paper "the sound of my voice speaker representation loss for target voice separation"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "deep attractor network for single microphone speaker separation"
    - "voxceleb a large scale speaker identification dataset"
    - "photographic image synthesis with cascaded refinement networks"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "improved techniques for training gans"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a predictive model for music based on learned interval representations"
    - "self normalizing neural networks"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-7 (induced by source paper "fast spectrogram inversion using multi head convolutional neural networks"):
  with members:
    - "neural voice cloning with a few samples"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice real time neural text to speech"
    - "a noniterative method for reconstruction of phase from stft magnitude"
    - "deep voice 3 2000 speaker neural text to speech"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "overview of tasks and investigation of subjective evaluation methods in environmental sound synthesis and conversion"):
  with members:
    - "deep learning techniques for music generation a survey"
    - "time domain neural audio style transfer"
    - "visual to sound generating natural sound for videos in the wild"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

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)