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: "curriculum learning and minibatch bucketing in neural machine translation"
  Abstract: We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called "curriculum learning"). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our "curricula" achieve a small improvement over the baseline.

Connected through hyperedge HE-1 (induced by source paper "learning to inpaint by progressively growing the mask regions"):
  with members:
    - "foreground aware image inpainting"
    - "competence based curriculum learning for neural machine translation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "improved training with curriculum gans"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (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-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 "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-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 "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"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "handling homographs in neural machine translation"

Connected through hyperedge HE-7 (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"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "dynamic data selection for neural machine translation"
    - "boosting neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning based curriculum optimization for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "reinforced co training"
    - "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"
    - "dynamic data selection for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "fast domain adaptation for 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)