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: "non parametric adaptation for neural machine translation"
  Abstract: Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the other hand, non-parametric approaches are immune to forgetting, perfectly complementing the generalization ability of NMT. However, attempts to combine non-parametric or retrieval based approaches with NMT have only been successful on narrow domains, possibly due to over-reliance on sentence level retrieval. We propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low. We complement this with an expressive neural network, allowing our model to extract information from the noisy retrieved context. We evaluate our semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT, JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets. The semi-parametric nature of our approach opens the door for non-parametric domain adaptation, demonstrating strong inference-time adaptation performance on new domains without the need for any parameter updates.

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"
    - "massively multilingual neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine 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 "domain differential adaptation for neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "a survey of domain adaptation for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "neural machine translation with noisy lexical constraints"):
  with members:
    - "training neural machine translation to apply terminology constraints"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "quickedit editing text translations by crossing words out"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-4 (induced by source paper "iterative dual domain adaptation for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "semantic neural machine translation using amr"
    - "a survey of domain adaptation for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "multi domain neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "instance based model adaptation for direct speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "the ustc nel speech translation system at iwslt 2018"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "guiding neural machine translation with retrieved translation pieces"

Connected through hyperedge HE-6 (induced by source paper "unsupervised domain adaptation for neural machine translation with domain aware feature embeddings"):
  with members:
    - "compare mt a tool for holistic comparison of language generation systems"
    - "a survey of domain adaptation for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "domain control for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised natural question answering with a small model"):
  with members:
    - "the curious case of neural text degeneration"
    - "learning and evaluating general linguistic intelligence"
    - "no training required exploring random encoders for sentence classification"
    - "a bert baseline for the natural questions"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to generate reviews and discovering sentiment"
    - "correcting length bias in 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)