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: "transfer learning for speech and language processing"
  Abstract: Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.

Connected through hyperedge HE-1 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "spectral signatures in backdoor attacks"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "a neural grammatical error correction system built on better pre training and sequential transfer learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "weakly supervised grammatical error correction using iterative decoding"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"

Connected through hyperedge HE-3 (induced by source paper "towards making the most of bert in neural machine translation"):
  with members:
    - "pre trained language model representations for language generation"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "lead2gold towards exploiting the full potential of noisy transcriptions for speech recognition"):
  with members:
    - "active and semi supervised learning in asr benefits on the acoustic and language models"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "a fully differentiable beam search decoder"
    - "wav2letter the fastest open source speech recognition system"
    - "label noise robust generative adversarial networks"
    - "letter based speech recognition with gated convnets"
    - "improving lstm ctc based asr performance in domains with limited training data"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-5 (induced by source paper "discriminative joint probability maximum mean discrepancy djp mmd for domain adaptation"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "supervised transfer learning for product information question answering"
    - "balanced distribution adaptation for transfer learning"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "neuromorphic hardware learns to learn"):
  with members:
    - "demonstrating advantages of neuromorphic computation a pilot study"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"

Connected through hyperedge HE-7 (induced by source paper "cantonese automatic speech recognition using transfer learning from mandarin"):
  with members:
    - "trivial transfer learning for low resource neural machine translation"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "transfer learning for speech recognition on a budget"

Connected through hyperedge HE-8 (induced by source paper "transfer learning oriented class imbalance learning for cross project defect prediction"):
  with members:
    - "an empirical study on software defect prediction with a simplified metric set"
    - "locally weighted naive bayes"
    - "smote synthetic minority over sampling technique"

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)