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: "conditional end to end audio transforms"
  Abstract: We present an end-to-end method for transforming audio from one style to another. For the case of speech, by conditioning on speaker identities, we can train a single model to transform words spoken by multiple people into multiple target voices. For the case of music, we can specify musical instruments and achieve the same result. Architecturally, our method is a fully-differentiable sequence-to-sequence model based on convolutional and hierarchical recurrent neural networks. It is designed to capture long-term acoustic dependencies, requires minimal post-processing, and produces realistic audio transforms. Ablation studies confirm that our model can separate speaker and instrument properties from acoustic content at different receptive fields. Empirically, our method achieves competitive performance on community-standard datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space"):
  with members:
    - "neuralogram a deep neural network based representation for audio signals"
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "learning to model aspects of hearing perception using neural loss functions"
    - "neural style transfer for audio spectograms"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "look listen and learn"

Connected through hyperedge HE-2 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"

Connected through hyperedge HE-3 (induced by source paper "direct speech to speech translation with a sequence to sequence model"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "end to end spoken language translation"
    - "tied multitask learning for neural speech translation"
    - "neural voice cloning with a few samples"
    - "end to end automatic speech translation of audiobooks"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"

Connected through hyperedge HE-4 (induced by source paper "neuralogram a deep neural network based representation for audio signals"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-5 (induced by source paper "learning to model aspects of hearing perception using neural loss functions"):
  with members:
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "audio adversarial examples targeted attacks on speech to text"
    - "learned perceptual image enhancement"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "play as you like timbre enhanced multi modal music style transfer"):
  with members:
    - "neural style transfer for audio spectograms"
    - "the relativistic discriminator a key element missing from standard gan"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "multimodal unsupervised image to image translation"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "generative face completion"

Connected through hyperedge HE-7 (induced by source paper "automatic documentation of icd codes with far field speech recognition"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "speech recognition for medical conversations"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a method for modeling co occurrence propensity of clinical codes with application to icd 10 pcs auto coding"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-8 (induced by source paper "sing symbol to instrument neural generator"):
  with members:
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "convolutional sequence to sequence learning"
    - "deepbach a steerable model for bach chorales generation"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"

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)