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: "reinforcement learning for bandit neural machine translation with simulated human feedback"
  Abstract: Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.

Connected through hyperedge HE-1 (induced by source paper "machine translation system selection from bandit feedback"):
  with members:
    - "extreme adaptation for personalized neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "overcoming catastrophic forgetting in neural networks"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"

Connected through hyperedge HE-3 (induced by source paper "towards coherent and cohesive long form text generation"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "unsupervised text style transfer using language models as discriminators"
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "coacor code annotation for code retrieval with reinforcement learning"):
  with members:
    - "comment generation for source code state of the art challenges and opportunities"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "visual question generation as dual task of visual question answering"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "preference based interactive multi document summarisation"):
  with members:
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "viana visual interactive annotation of argumentation"
    - "better rewards yield better summaries learning to summarise without references"
    - "improving abstraction in text summarization"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "counterfactual learning from human proofreading feedback for semantic parsing"
    - "can neural machine translation be improved with user feedback"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "machine translation for machines the sentiment classification use case"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "can neural machine translation be improved with user feedback"
    - "a study of reinforcement learning for neural machine translation"
    - "getting gender right in neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "six challenges 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)