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: "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
  Abstract: The performance of automatic speech recognition (ASR) systems can be significantly compromised by previously unseen conditions, which is typically due to a mismatch between training and testing distributions. In this paper, we address robustness by studying domain invariant features, such that domain information becomes transparent to ASR systems, resolving the mismatch problem. Specifically, we investigate a recent model, called the Factorized Hierarchical Variational Autoencoder (FHVAE). FHVAEs learn to factorize sequence-level and segment-level attributes into different latent variables without supervision. We argue that the set of latent variables that contain segment-level information is our desired domain invariant feature for ASR. Experiments are conducted on Aurora-4 and CHiME-4, which demonstrate 41% and 27% absolute word error rate reductions respectively on mismatched domains.

Connected through hyperedge HE-1 (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-2 (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"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "unsupervised textual grounding linking words to image concepts"
    - "jointly discovering visual objects and spoken words from raw sensory input"

Connected through hyperedge HE-3 (induced by source paper "niesr nuisance invariant end to end speech recognition"):
  with members:
    - "unified adversarial invariance"
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "very deep convolutional networks for end to end speech recognition"
    - "invariant representations for noisy speech recognition"
    - "listen attend and spell"
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"):
  with members:
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "tackling over pruning in variational autoencoders"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-5 (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"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational 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-6 (induced by source paper "domain mismatch robust acoustic scene classification using channel information conversion"):
  with members:
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"

Connected through hyperedge HE-7 (induced by source paper "a study of enhancement augmentation and autoencoder methods for domain adaptation in distant speech recognition"):
  with members:
    - "highway long short term memory rnns for distant speech recognition"
    - "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.CL(Computation and Language)