human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "extracting domain invariant features by unsupervised learning for robust automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "unsupervised adaptation with domain separation networks for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "residual lstm design of a deep recurrent architecture for distant speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "tackling over pruning in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "balancing reconstruction error and kullback leibler divergence in variational autoencoders")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "learning latent representations for speech generation and transformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep autotuner a pitch correcting network for singing performances")
  Member "unsupervised learning of disentangled and interpretable representations from sequential data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "learning hidden unit contributions for unsupervised acoustic model adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "speaker adaptation for end to end ctc models")

Question: Please tell me which class the source paper 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)