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: "neural voice cloning with a few samples"
  Abstract: Voice cloning is a highly desired feature for personalized speech interfaces. Neural network based speech synthesis has been shown to generate high quality speech for a large number of speakers. In this paper, we introduce a neural voice cloning system that takes a few audio samples as input. We study two approaches: speaker adaptation and speaker encoding. Speaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples. Speaker encoding is based on training a separate model to directly infer a new speaker embedding from cloning audios and to be used with a multi-speaker generative model. In terms of naturalness of the speech and its similarity to original speaker, both approaches can achieve good performance, even with very few cloning audios. While speaker adaptation can achieve better naturalness and similarity, the cloning time or required memory for the speaker encoding approach is significantly less, making it favorable for low-resource deployment.

Connected through hyperedge HE-1 (induced by source paper "speech2phone a multilingual and text independent speaker identification model"):
  with members:
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep voice 3 2000 speaker neural text to speech"
    - "tristounet triplet loss for speaker turn embedding"
    - "tf learn tensorflow s high level module for distributed machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "a plug in method for representation factorization"):
  with members:
    - "seeing what a gan cannot generate"
    - "wasserstein dependency measure for representation learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "pioneer networks progressively growing generative autoencoder"

Connected through hyperedge HE-3 (induced by source paper "learning to transfer learn"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "object part attention model for fine grained image classification"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "domain adaptive transfer learning with specialist models"
    - "spottune transfer learning through adaptive fine tuning"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "parallel neural text to speech"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "efficient neural audio synthesis"
    - "stochastic video generation with a learned prior"
    - "sample efficient adaptive text to speech"

Connected through hyperedge HE-5 (induced by source paper "incremental learning using a grow and prune paradigm with efficient neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "knowledge transfer in deep block modular neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "incremental training of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "few shot adversarial learning of realistic neural talking head models"):
  with members:
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "deep video portraits"
    - "arcface additive angular margin loss for deep face recognition"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "voxceleb2 deep speaker recognition"

Connected through hyperedge HE-7 (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"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-8 (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"
    - "end to end automatic speech translation of audiobooks"
    - "efficient neural audio synthesis"
    - "conditional end to end audio transforms"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"

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.CL(Computation and Language)