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: "attentive adversarial learning for domain invariant training"
  Abstract: Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, ADDIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.

Connected through hyperedge HE-1 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "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"
    - "end to end feedback loss in speech chain framework via straight through estimator"

Connected through hyperedge HE-2 (induced by source paper "speaker adaptation for attention based end to end speech recognition"):
  with members:
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "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-3 (induced by source paper "conditional teacher student learning"):
  with members:
    - "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"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial speaker adaptation"):
  with members:
    - "conditional teacher student learning"
    - "adversarial speaker verification"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "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"

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.LG(Machine Learning)