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 adaptation with interpretable disentangled representations for distant conversational speech recognition"
  Abstract: The current trend in automatic speech recognition is to leverage large amounts of labeled data to train supervised neural network models. Unfortunately, obtaining data for a wide range of domains to train robust models can be costly. However, it is relatively inexpensive to collect large amounts of unlabeled data from domains that we want the models to generalize to. In this paper, we propose a novel unsupervised adaptation method that learns to synthesize labeled data for the target domain from unlabeled in-domain data and labeled out-of-domain data. We first learn without supervision an interpretable latent representation of speech that encodes linguistic and nuisance factors (e.g., speaker and channel) using different latent variables. To transform a labeled out-of-domain utterance without altering its transcript, we transform the latent nuisance variables while maintaining the linguistic variables. To demonstrate our approach, we focus on a channel mismatch setting, where the domain of interest is distant conversational speech, and labels are only available for close-talking speech. Our proposed method is evaluated on the AMI dataset, outperforming all baselines and bridging the gap between unadapted and in-domain models by over 77% without using any parallel data.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "an unsupervised autoregressive model for speech representation learning"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised textual grounding linking words to image concepts"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-2 (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"
    - "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"
    - "unsupervised domain adaptation for robust speech recognition via variational autoencoder based data augmentation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "towards learning fine grained disentangled representations from speech"):
  with members:
    - "a study of enhancement augmentation and autoencoder methods for domain adaptation in distant speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "face aging with conditional generative adversarial networks"
    - "jade joint autoencoders for dis entanglement"
    - "fader networks manipulating images by sliding attributes"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "domain mismatch robust acoustic scene classification using channel information conversion"):
  with members:
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"

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)