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: "active learning for interactive neural machine translation of data streams"
  Abstract: We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. #R##N#We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.

Connected through hyperedge HE-1 (induced by source paper "selection via proxy efficient data selection for deep learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "weight agnostic neural networks"
    - "how transferable are the datasets collected by active learners"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-2 (induced by source paper "self regulated interactive sequence to sequence learning"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "joey nmt a minimalist nmt toolkit for novices"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "learning to teach with dynamic loss functions"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "learning from chunk based feedback in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "semi supervised sequence modeling with cross view training"

Connected through hyperedge HE-3 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "convolutional sequence to sequence learning"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "pre translation for neural machine translation"
    - "systran s pure neural machine translation systems"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"

Connected through hyperedge HE-4 (induced by source paper "correct and memorize learning to translate from interactive revisions"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "learning to discriminate noises for incorporating external information in neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "online learning for neural machine translation post editing"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-5 (induced by source paper "interactive predictive neural machine translation through reinforcement and imitation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "compact personalized models for neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "fast policy learning through imitation and reinforcement"
    - "online learning for neural machine translation post editing"

Connected through hyperedge HE-6 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "commonsense for generative multi hop question answering tasks"

Connected through hyperedge HE-7 (induced by source paper "online learning for effort reduction in interactive neural machine translation"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "trainable greedy decoding for neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "online learning for effort reduction in interactive neural machine translation"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural machine translation of rare words with subword units"
    - "microsoft coco captions data collection and evaluation server"

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)