human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "neural voice cloning with a few samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speech2phone a multilingual and text independent speaker identification model")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "transfer learning from speaker verification to multispeaker text to speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel neural text to speech")
  Member "observe and look further achieving consistent performance on atari":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "fitting new speakers based on a short untranscribed sample":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "one shot imitation from observing humans via domain adaptive meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting meta learning as supervised learning")
  Member "towards end to end prosody transfer for expressive speech synthesis with tacotron":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks")
  Member "voiceloop voice fitting and synthesis via a phonological loop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")

Question: Please tell me which class the source paper 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)