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: "sampling based speech parameter generation using moment matching networks"
  Abstract: This paper presents sampling-based speech parameter generation using moment-matching networks for Deep Neural Network (DNN)-based speech synthesis. Although people never produce exactly the same speech even if we try to express the same linguistic and para-linguistic information, typical statistical speech synthesis produces completely the same speech, i.e., there is no inter-utterance variation in synthetic speech. To give synthetic speech natural inter-utterance variation, this paper builds DNN acoustic models that make it possible to randomly sample speech parameters. The DNNs are trained so that they make the moments of generated speech parameters close to those of natural speech parameters. Since the variation of speech parameters is compressed into a low-dimensional simple prior noise vector, our algorithm has lower computation cost than direct sampling of speech parameters. As the first step towards generating synthetic speech that has natural inter-utterance variation, this paper investigates whether or not the proposed sampling-based generation deteriorates synthetic speech quality. In evaluation, we compare speech quality of conventional maximum likelihood-based generation and proposed sampling-based generation. The result demonstrates the proposed generation causes no degradation in speech quality.

Connected through hyperedge HE-1 (induced by source paper "generative moment matching network based random modulation post filter for dnn based singing voice synthesis and neural double tracking"):
  with members:
    - "language modeling with gated convolutional networks"
    - "generative moment matching networks"

Connected through hyperedge HE-2 (induced by source paper "jvs corpus free japanese multi speaker voice corpus"):
  with members:
    - "statistical parametric speech synthesis incorporating generative adversarial networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "phase reconstruction from amplitude spectrograms based on von mises distribution deep neural network"):
  with members:
    - "tacotron towards end to end speech synthesis"
    - "jsut corpus free large scale japanese speech corpus for end to end speech synthesis"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"
    - "language modeling with gated convolutional networks"
    - "generative moment matching networks"

Connected through hyperedge HE-4 (induced by source paper "jsut corpus free large scale japanese speech corpus for end to end speech synthesis"):
  with members:
    - "tacotron towards end to end speech synthesis"
    - "wavenet a generative model for raw audio"

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)