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: "unsupervised domain adaptation for robust speech recognition via variational autoencoder based data augmentation"
  Abstract: Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are presented, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.

Connected through hyperedge HE-1 (induced by source paper "personalization of deep learning"):
  with members:
    - "human to ai coach improving human inputs to ai systems"
    - "personalized explanation in machine learning"
    - "transfer adaptation learning a decade survey"
    - "explainable recommendation a survey and new perspectives"
    - "fairness through computationally bounded awareness"
    - "small sample learning in big data era"
    - "automated curriculum learning for neural networks"
    - "learning by asking questions"

Connected through hyperedge HE-2 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "parallel neural text to speech"
    - "libri light a benchmark for asr with limited or no supervision"
    - "lessons from building acoustic models with a million hours of speech"
    - "a fully differentiable beam search decoder"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "gee a gradient based explainable variational autoencoder for network anomaly detection"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-4 (induced by source paper "characterizing audio adversarial examples using temporal dependency"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "on the limitation of magnet defense against l_1 based adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "hierarchical generative modeling for controllable speech synthesis"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "neural voice cloning with a few samples"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "fitting new speakers based on a short untranscribed sample"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"):
  with members:
    - "multi agent diverse generative adversarial networks"
    - "improving end to end speech recognition with policy learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-7 (induced by source paper "convolutional vs recurrent neural networks for audio source separation"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "adversarial discriminative domain adaptation"
    - "single channel multi speaker separation using deep clustering"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-8 (induced by source paper "training augmentation with adversarial examples for robust speech recognition"):
  with members:
    - "distilling knowledge using parallel data for far field speech recognition"
    - "domain adversarial training for accented speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial examples for evaluating reading comprehension systems"
    - "adversarial machine learning at scale"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

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.CL(Computation and Language)