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: "a reinforcement learning approach to interactive predictive neural machine translation"
  Abstract: We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of $5$ feedback requests for every input.

Connected through hyperedge HE-1 (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"
    - "active learning for interactive neural machine translation of data streams"
    - "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-2 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "towards diverse text generation with inverse reinforcement learning"
    - "a machine learning approach to persian text readability assessment using a crowdsourced dataset"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "active learning for interactive neural machine translation of data streams"
    - "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-4 (induced by source paper "incremental adaptation of nmt for professional post editors a user study"):
  with members:
    - "online learning for effort reduction in interactive neural machine translation"
    - "compact personalized models for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "a user study on online adaptation of neural machine translation to human post edits"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "online learning for effort reduction in interactive neural machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "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-6 (induced by source paper "active learning for interactive neural machine translation of data streams"):
  with members:
    - "online learning for effort reduction in interactive neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "learning from chunk based feedback in neural machine translation"):
  with members:
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "asynchronous methods for deep reinforcement learning"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "a user study on online adaptation of neural machine translation to human post edits"):
  with members:
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "quantitative fine grained human evaluation of machine translation systems a case study on english to croatian"
    - "can neural machine translation be improved with user feedback"
    - "a challenge set approach to evaluating machine translation"
    - "fine grained human evaluation of neural versus phrase based machine translation"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "six challenges for neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

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)