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: "analyzing uncertainty in neural machine translation"
  Abstract: Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that the models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. We release both code and multiple human reference translations for two popular benchmarks.

Connected through hyperedge HE-1 (induced by source paper "discriminative adversarial search for abstractive summarization"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "answers unite unsupervised metrics for reinforced summarization models"
    - "defending against neural fake news"
    - "bridging the gap between training and inference for neural machine translation"
    - "better rewards yield better summaries learning to summarise without references"
    - "pytext a seamless path from nlp research to production"
    - "bottom up abstractive summarization"
    - "language gans falling short"

Connected through hyperedge HE-2 (induced by source paper "ask to learn a study on curiosity driven question generation"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "question answering as an automatic evaluation metric for news article summarization"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "towards a better metric for evaluating question generation systems"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "two new evaluation datasets for low resource machine translation nepali english and sinhala english"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised statistical machine translation"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "simple fusion return of the language model"
    - "scaling neural machine translation"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-4 (induced by source paper "generalizing back translation in neural machine translation"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "explaining and generalizing back translation through wake sleep"
    - "understanding back translation at scale"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "regularizing neural networks by penalizing confident output distributions"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "frage frequency agnostic word representation"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "unsupervised machine translation using monolingual corpora only"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "adversarial discriminative domain adaptation"
    - "regularizing and optimizing lstm language models"
    - "classical structured prediction losses for sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "non autoregressive neural machine translation with enhanced decoder input"):
  with members:
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "fast decoding in sequence models using discrete latent variables"
    - "double path networks for sequence to sequence learning"
    - "dense information flow for neural machine translation"
    - "attention is all you need"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "language gans falling short"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "neural text generation past present and beyond"
    - "adversarial text generation via feature mover s distance"
    - "towards diverse text generation with inverse reinforcement learning"
    - "cot cooperative training for generative modeling of discrete data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-8 (induced by source paper "quantum statistics inspired neural attention"):
  with members:
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "enhancing sentence embedding with generalized pooling"
    - "translating phrases in neural machine translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "structured attention networks"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "end to end memory networks"

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)