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: "language and noise transfer in speech enhancement generative adversarial network"
  Abstract: Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by finetuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean. We also study the variability of test performance to unseen noise as a function of the amount of different types of noise available for training. Results show that adapting a pre-trained English model with 10 min of data already achieves a comparable performance to having two orders of magnitude more data. They also demonstrate the relative stability in test performance with respect to the number of training noise types.

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"
    - "boosting noise robustness of acoustic model via deep adversarial training"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-2 (induced by source paper "cochleanet a robust language independent audio visual model for speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sdr half baked or well done"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"
    - "the sound of pixels"
    - "lip reading driven deep learning approach for speech enhancement"
    - "audio visual scene analysis with self supervised multisensory features"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "towards generalized speech enhancement with generative adversarial networks"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "clarinet parallel wave generation in end to end text to speech"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"

Connected through hyperedge HE-4 (induced by source paper "training neural audio classifiers with few data"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "deep learning for audio transcription on low resource datasets"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "unsupervised learning of semantic audio representations"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"

Connected through hyperedge HE-5 (induced by source paper "whispered to voiced alaryngeal speech conversion with generative adversarial networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "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)