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: "boosting noise robustness of acoustic model via deep adversarial training"
  Abstract: In realistic environments, speech is usually interfered by various noise and reverberation, which dramatically degrades the performance of automatic speech recognition (ASR) systems. To alleviate this issue, the commonest way is to use a well-designed speech enhancement approach as the front-end of ASR. However, more complex pipelines, more computations and even higher hardware costs (microphone array) are additionally consumed for this kind of methods. In addition, speech enhancement would result in speech distortions and mismatches to training. In this paper, we propose an adversarial training method to directly boost noise robustness of acoustic model. Specifically, a jointly compositional scheme of generative adversarial net (GAN) and neural network-based acoustic model (AM) is used in the training phase. GAN is used to generate clean feature representations from noisy features by the guidance of a discriminator that tries to distinguish between the true clean signals and generated signals. The joint optimization of generator, discriminator and AM concentrates the strengths of both GAN and AM for speech recognition. Systematic experiments on CHiME-4 show that the proposed method significantly improves the noise robustness of AM and achieves the average relative error rate reduction of 23.38% and 11.54% on the development and test set, respectively.

Connected through hyperedge HE-1 (induced by source paper "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks"):
  with members:
    - "transferable adversarial robustness using adversarially trained autoencoders"
    - "a computationally efficient method for defending adversarial deep learning attacks"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "breaking transferability of adversarial samples with randomness"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "enhancing robustness of machine learning systems via data transformations"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "ufans u shaped fully parallel acoustic neural structure for statistical parametric speech synthesis with 20x faster"):
  with members:
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "a neural parametric singing synthesizer"
    - "opening the black box of deep neural networks via information"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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)