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: "dual conditional cross entropy filtering of noisy parallel corpora"
  Abstract: In this work we introduce dual conditional cross-entropy filtering for noisy parallel data. For each sentence pair of the noisy parallel corpus we compute cross-entropy scores according to two inverse translation models trained on clean data. We penalize divergent cross-entropies and weigh the penalty by the cross-entropy average of both models. Sorting or thresholding according to these scores results in better subsets of parallel data. We achieve higher BLEU scores with models trained on parallel data filtered only from Paracrawl than with models trained on clean WMT data. We further evaluate our method in the context of the WMT2018 shared task on parallel corpus filtering and achieve the overall highest ranking scores of the shared task, scoring top in three out of four subtasks.

Connected through hyperedge HE-1 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "algorithms that remember model inversion attacks and data protection law"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "understanding membership inferences on well generalized learning models"
    - "the unintended consequences of overfitting training data inference attacks"
    - "knock knock who s there membership inference on aggregate location data"

Connected through hyperedge HE-2 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"

Connected through hyperedge HE-3 (induced by source paper "learning a multitask curriculum for neural machine translation"):
  with members:
    - "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"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for 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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "learning bilingual sentence embeddings via autoencoding and computing similarities with a multilayer perceptron"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"

Connected through hyperedge HE-5 (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"
    - "freezing subnetworks to analyze domain adaptation in 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"

Connected through hyperedge HE-6 (induced by source paper "low resource corpus filtering using multilingual sentence embeddings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "on the impact of various types of noise on neural machine translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a call for clarity in reporting bleu scores"
    - "phrase based neural unsupervised machine translation"
    - "margin based parallel corpus mining with multilingual sentence embeddings"

Connected through hyperedge HE-7 (induced by source paper "uniblock scoring and filtering corpus with unicode block information"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "a call for clarity in reporting bleu scores"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-8 (induced by source paper "microsoft translator at wmt 2019 towards large scale document level neural machine translation"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "understanding back translation at scale"
    - "neural machine translation with extended context"
    - "attention is all you need"

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)