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: "freezing subnetworks to analyze domain adaptation in neural machine translation"
  Abstract: To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component's contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.

Connected through hyperedge HE-1 (induced by source paper "simple scalable adaptation for neural machine translation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "non parametric adaptation for neural machine translation"
    - "massively multilingual neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "a survey of domain adaptation for neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "compact personalized models for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "positional encoding to control output sequence length"
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "multi task neural models for translating between styles within and across languages"
    - "a survey of domain adaptation for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "scaling neural machine translation"
    - "neural machine translation into language varieties"

Connected through hyperedge HE-3 (induced by source paper "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-4 (induced by source paper "transfer learning across languages from someone else s nmt model"):
  with members:
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "a survey on deep transfer learning"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"

Connected through hyperedge HE-5 (induced by source paper "multilingual multi domain adaptation approaches for neural machine translation"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "how transferable are neural networks in nlp applications"

Connected through hyperedge HE-6 (induced by source paper "robust neural machine translation for clean and noisy speech transcripts"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "punctuation prediction model for conversational speech"
    - "attention is all you need"
    - "mitigating the impact of speech recognition errors on chatbot using sequence to sequence model"
    - "synthetic and natural noise both break neural machine translation"
    - "assessing the tolerance of neural machine translation systems against speech recognition errors"

Connected through hyperedge HE-7 (induced by source paper "domain adaptive inference for neural machine translation"):
  with members:
    - "a call for clarity in reporting bleu scores"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "regularization techniques for fine tuning in neural machine translation"
    - "six challenges for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-8 (induced by source paper "the sigmorphon 2019 shared task morphological analysis in context and cross lingual transfer for inflection"):
  with members:
    - "exact hard monotonic attention for character level transduction"
    - "neural factor graph models for cross lingual morphological tagging"
    - "marrying universal dependencies and universal morphology"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid adaptation of neural machine translation to new languages"
    - "hard non monotonic attention for character level transduction"
    - "transfer learning for low resource neural machine translation"
    - "effective approaches to attention based neural machine translation"

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)