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: "better rewards yield better summaries learning to summarise without references"
  Abstract: Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL-based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings. The learned reward function and our source code are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "learning by semantic similarity makes abstractive summarization better"):
  with members:
    - "prophetnet predicting future n gram for sequence to sequence pre training"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "summary level training of sentence rewriting for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
    - "bottom up abstractive summarization"
    - "adversarial text generation via feature mover s distance"

Connected through hyperedge HE-2 (induced by source paper "discriminative adversarial search for abstractive summarization"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "answers unite unsupervised metrics for reinforced summarization models"
    - "defending against neural fake news"
    - "bridging the gap between training and inference for neural machine translation"
    - "pytext a seamless path from nlp research to production"
    - "analyzing uncertainty in neural machine translation"
    - "bottom up abstractive summarization"
    - "language gans falling short"

Connected through hyperedge HE-3 (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-4 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-5 (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"
    - "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"
    - "finding convincing arguments using scalable bayesian preference learning"

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)