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: "large scale domain adaptation via teacher student learning"
  Abstract: High accuracy speech recognition requires a large amount of transcribed data for supervised training. In the absence of such data, domain adaptation of a well-trained acoustic model can be performed, but even here, high accuracy usually requires significant labeled data from the target domain. In this work, we propose an approach to domain adaptation that does not require transcriptions but instead uses a corpus of unlabeled parallel data, consisting of pairs of samples from the source domain of the well-trained model and the desired target domain. To perform adaptation, we employ teacher/student (T/S) learning, in which the posterior probabilities generated by the source-domain model can be used in lieu of labels to train the target-domain model. We evaluate the proposed approach in two scenarios, adapting a clean acoustic model to noisy speech and adapting an adults speech acoustic model to children speech. Significant improvements in accuracy are obtained, with reductions in word error rate of up to 44% over the original source model without the need for transcribed data in the target domain. Moreover, we show that increasing the amount of unlabeled data results in additional model robustness, which is particularly beneficial when using simulated training data in the target-domain.

Connected through hyperedge HE-1 (induced by source paper "fully learnable front end for multi channel acoustic modeling using semi supervised learning"):
  with members:
    - "lessons from building acoustic models with a million hours of speech"
    - "multichannel end to end speech recognition"

Connected through hyperedge HE-2 (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"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "sequence level knowledge distillation"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-3 (induced by source paper "lessons from building acoustic models with a million hours of speech"):
  with members:
    - "on calibration of modern neural networks"
    - "empirical evaluation of parallel training algorithms on acoustic modeling"
    - "knowledge distillation for small footprint highway networks"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "large scale distributed acoustic modeling with back off rm n grams"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-4 (induced by source paper "progressive joint modeling in unsupervised single channel overlapped speech recognition"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "recognizing multi talker speech with permutation invariant training"
    - "small footprint highway deep neural networks for speech recognition"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-5 (induced by source paper "toward domain invariant speech recognition via large scale training"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "adversarial discriminative domain adaptation"
    - "comparing human and machine errors in conversational speech transcription"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "developing far field speaker system via teacher student learning"):
  with members:
    - "advancing acoustic to word ctc model"
    - "advancing connectionist temporal classification with attention modeling"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "maximum a posteriori adaptation of network parameters in deep models"
    - "transferring knowledge from a rnn to a dnn"

Connected through hyperedge HE-7 (induced by source paper "cracking the cocktail party problem by multi beam deep attractor network"):
  with members:
    - "speaker independent speech separation with deep attractor network"
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi speaker separation using deep clustering"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

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)