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 domain separation networks for robust speech recognition"
  Abstract: Unsupervised domain adaptation of speech signal aims at adapting a well-trained source-domain acoustic model to the unlabeled data from target domain. This can be achieved by adversarial training of deep neural network (DNN) acoustic models to learn an intermediate deep representation that is both senone-discriminative and domain-invariant. Specifically, the DNN is trained to jointly optimize the primary task of senone classification and the secondary task of domain classification with adversarial objective functions. In this work, instead of only focusing on learning a domain-invariant feature (i.e. the shared component between domains), we also characterize the difference between the source and target domain distributions by explicitly modeling the private component of each domain through a private component extractor DNN. The private component is trained to be orthogonal with the shared component and thus implicitly increases the degree of domain-invariance of the shared component. A reconstructor DNN is used to reconstruct the original speech feature from the private and shared components as a regularization. This domain separation framework is applied to the unsupervised environment adaptation task and achieved 11.08% relative WER reduction from the gradient reversal layer training, a representative adversarial training method, for automatic speech recognition on CHiME-3 dataset.

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 "attentive adversarial learning for domain invariant training"):
  with members:
    - "conditional teacher student learning"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "domain adversarial training for accented speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "english conversational telephone speech recognition by humans and machines"
    - "attention is all you need"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "speaker adaptation for attention based end to end speech recognition"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "state of the art speech recognition with sequence to sequence models"
    - "segan speech enhancement generative adversarial network"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-4 (induced by source paper "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "developing far field speaker system via teacher student learning"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a teacher student framework for zero resource neural machine translation"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial speaker verification"):
  with members:
    - "adversarial speaker adaptation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "english conversational telephone speech recognition by humans and machines"
    - "segan speech enhancement generative adversarial network"
    - "deep speaker an end to end neural speaker embedding system"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "end to end attention based text dependent speaker verification"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-6 (induced by source paper "adversarial speaker adaptation"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "conditional teacher student learning"
    - "adversarial speaker verification"
    - "english conversational telephone speech recognition by humans and machines"
    - "segan speech enhancement generative adversarial network"
    - "domain separation networks"
    - "invariant representations for noisy speech recognition"
    - "maximum a posteriori adaptation of network parameters in deep models"

Connected through hyperedge HE-7 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-8 (induced by source paper "noise adaptive speech enhancement using domain adversarial training"):
  with members:
    - "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"
    - "domain adversarial training for accented speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "adversarial discriminative domain adaptation"
    - "tacotron towards end to end speech synthesis"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "speech enhancement in multiple noise conditions using deep 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)