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: "cyclegan vc2 improved cyclegan based non parallel voice conversion"
  Abstract: Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without relying on any extra data, modules, or time alignment procedures. However, there is still a large gap between the real target and converted speech, and bridging this gap remains a challenge. To reduce this gap, we propose CycleGAN-VC2, which is an improved version of CycleGAN-VC incorporating three new techniques: an improved objective (two-step adversarial losses), improved generator (2-1-2D CNN), and improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC task and analyzed the effect of each technique in detail. An objective evaluation showed that these techniques help bring the converted feature sequence closer to the target in terms of both global and local structures, which we assess by using Mel-cepstral distortion and modulation spectra distance, respectively. A subjective evaluation showed that CycleGAN-VC2 outperforms CycleGAN-VC in terms of naturalness and similarity for every speaker pair, including intra-gender and inter-gender pairs.

Connected through hyperedge HE-1 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders"):
  with members:
    - "stargan vc2 rethinking conditional methods for stargan based voice conversion"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "text based editing of talking head video"
    - "learning individual styles of conversational gesture"
    - "speech2face learning the face behind a voice"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"

Connected through hyperedge HE-2 (induced by source paper "many to many voice conversion using conditional cycle consistent adversarial networks"):
  with members:
    - "stargan vc2 rethinking conditional methods for stargan based voice conversion"
    - "efficient neural audio synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "neural discrete representation learning"
    - "language modeling with gated convolutional networks"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks"):
  with members:
    - "talking face generation by adversarially disentangled audio visual representation"
    - "voxceleb2 deep speaker recognition"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voxceleb a large scale speaker identification dataset"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "emotional end to end neural speech synthesizer"
    - "cnn architectures for large scale audio classification"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a style transfer approach to source separation"):
  with members:
    - "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"
    - "audio style transfer"
    - "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-5 (induced by source paper "stargan vc2 rethinking conditional methods for stargan based voice conversion"):
  with members:
    - "cgans with projection discriminator"
    - "convs2s vc fully convolutional sequence to sequence voice conversion"
    - "class distinct and class mutual image generation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-6 (induced by source paper "eg gan cross language emotion gain synthesis based on cycle consistent adversarial networks"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "emotional end to end neural speech synthesizer"
    - "instance normalization the missing ingredient for fast stylization"
    - "nips 2016 tutorial generative adversarial networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "emotionless privacy preserving speech analysis for voice assistants"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "multi task self supervised visual learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)