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: "representation mixing for tts synthesis"
  Abstract: Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.

Connected through hyperedge HE-1 (induced by source paper "libritts a corpus derived from librispeech for text to speech"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "efficient neural audio synthesis"
    - "sample efficient adaptive text to speech"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "voice imitating text to speech neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning to speak fluently in a foreign language multilingual speech synthesis and cross language voice cloning"):
  with members:
    - "unsupervised polyglot text to speech"
    - "neural voice cloning with a few samples"
    - "efficient neural audio synthesis"
    - "sample efficient adaptive text to speech"
    - "learning pronunciation from a foreign language in speech synthesis networks"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "fitting new speakers based on a short untranscribed sample"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "location relative attention mechanisms for robust long form speech synthesis"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "effective use of variational embedding capacity in expressive end to end speech synthesis"
    - "efficient neural audio synthesis"
    - "robust and fine grained prosody control of end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "online and linear time attention by enforcing monotonic alignments"
    - "tacotron towards end to end speech synthesis"
    - "attention based models for speech recognition"

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)