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: "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
  Abstract: We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator, and the effect of the quality of reward estimates on the overall RL task. Our analysis of cardinal (5-point ratings) and ordinal (pairwise preferences) feedback shows that their intra- and inter-annotator $\alpha$-agreement is comparable. Best reliability is obtained for standardized cardinal feedback, and cardinal feedback is also easiest to learn and generalize from. Finally, improvements of over 1 BLEU can be obtained by integrating a regression-based reward estimator trained on cardinal feedback for 800 translations into RL for NMT. This shows that RL is possible even from small amounts of fairly reliable human feedback, pointing to a great potential for applications at larger scale.

Connected through hyperedge HE-1 (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-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"
    - "learning to teach with dynamic loss functions"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "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-3 (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"
    - "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"
    - "finding convincing arguments using scalable bayesian preference learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "online learning for neural machine translation post editing"

Connected through hyperedge HE-5 (induced by source paper "modeling fluency and faithfulness for diverse neural machine translation"):
  with members:
    - "bridging the gap between training and inference for neural machine translation"
    - "synchronous bidirectional inference for neural sequence generation"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "a study of reinforcement learning for neural machine translation"
    - "greedy search with probabilistic n gram matching for neural machine translation"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "better rewards yield better summaries learning to summarise without references"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-7 (induced by source paper "reward learning for efficient reinforcement learning in extractive document summarisation"):
  with members:
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "reward learning from human preferences and demonstrations in atari"
    - "fast policy learning through imitation and reinforcement"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

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:
    - "quantitative fine grained human evaluation of machine translation systems a case study on english to croatian"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "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)