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: "boosting neural machine translation"
  Abstract: Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning "difficult" concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-2 (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-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 "competence based curriculum learning for neural machine translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "training tips for the transformer model"
    - "contextual parameter generation for universal neural machine translation"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

Connected through hyperedge HE-5 (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"
    - "dynamic data selection for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "an empirical exploration of curriculum learning for neural machine translation"):
  with members:
    - "scheduled multi task learning from syntax to translation"
    - "automated curriculum learning for neural networks"
    - "sockeye a toolkit for neural machine translation"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "dynamic data selection for neural machine translation"
    - "six challenges for neural machine translation"
    - "visualizing and understanding curriculum learning for long short term memory networks"
    - "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"

Connected through hyperedge HE-7 (induced by source paper "dynamic sentence sampling for efficient training of neural machine translation"):
  with members:
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "attention is all you need"
    - "dynamic data selection for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "neural machine translation in linear time"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-8 (induced by source paper "dynamic data selection for neural machine translation"):
  with members:
    - "data augmentation for low resource neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "six challenges for neural machine translation"
    - "learning to select data for transfer learning with bayesian optimization"
    - "fast domain adaptation for neural machine translation"
    - "neural machine translation in linear time"
    - "systran s pure neural machine translation systems"
    - "transfer learning for low resource 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)