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: "sample efficient adaptive text to speech"
  Abstract: We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.

Connected through hyperedge HE-1 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"
    - "online learning with gated linear networks"

Connected through hyperedge HE-2 (induced by source paper "mel spectrogram augmentation for sequence to sequence voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "sequence to sequence acoustic modeling for voice conversion"
    - "tacotron towards end to end speech synthesis"
    - "is the deconvolution layer the same as a convolutional layer"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "modeling friends and foes"
    - "deep online learning via meta learning continual adaptation for model based rl"

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"
    - "neural voice cloning with a few samples"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-6 (induced by source paper "modular meta learning with shrinkage"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "optimizing millions of hyperparameters by implicit differentiation"
    - "meta learning with differentiable convex optimization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-7 (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"
    - "conditional end to end audio transforms"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised disentanglement with guarantees"):
  with members:
    - "weakly supervised disentanglement by pairwise similarities"
    - "demystifying inter class disentanglement"
    - "latent optimization for non adversarial representation disentanglement"
    - "cgans with projection discriminator"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"

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.LG(Machine Learning)