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: "dynamic data selection for neural machine translation"
  Abstract: Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce dynamic data selection for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call gradual fine-tuning, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.

Connected through hyperedge HE-1 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for 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"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"

Connected through hyperedge HE-2 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "domain adversarial training for accented speech recognition"
    - "translating pro drop languages with reconstruction models"
    - "adversarial multi criteria learning for chinese word segmentation"

Connected through hyperedge HE-3 (induced by source paper "autosem automatic task selection and mixing in multi task learning"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "dynamic multi level multi task learning for sentence simplification"
    - "automated curriculum learning for neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "adapting multilingual neural machine translation to unseen languages"):
  with members:
    - "generalized data augmentation for low resource translation"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "universal neural machine translation for extremely low resource languages"
    - "scaling neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "curriculum learning for domain adaptation in neural machine translation"):
  with members:
    - "data selection with cluster based language difference models and cynical selection"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-7 (induced by source paper "revisiting simple domain adaptation methods in unsupervised neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "domain adaptive inference for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "data ordering patterns for neural machine translation an empirical study"):
  with members:
    - "competence based curriculum learning for neural machine translation"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "automated curriculum learning for neural networks"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "boosting 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)