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: "guiding extractive summarization with question answering rewards"
  Abstract: Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth. Manual annotation of extraction units is cost-prohibitive, whereas acquiring labels by automatically aligning human abstracts and source documents can yield inferior results. In this paper we describe a novel framework to guide a supervised, extractive summarization system with question-answering rewards. We argue that quality summaries should serve as a document surrogate to answer important questions, and such question-answer pairs can be conveniently obtained from human abstracts. The system learns to promote summaries that are informative, fluent, and perform competitively on question-answering. Our results compare favorably with those reported by strong summarization baselines as evaluated by automatic metrics and human assessors.

Connected through hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"

Connected through hyperedge HE-2 (induced by source paper "finding generalizable evidence by learning to convince q a models"):
  with members:
    - "question answering as an automatic evaluation metric for news article summarization"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "cicero multi turn contextual argumentation for accurate crowdsourcing"
    - "annotation artifacts in natural language inference data"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "what makes reading comprehension questions easier"
    - "generating natural language adversarial examples"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "rethinking cooperative rationalization introspective extraction and complement control"):
  with members:
    - "interpretable neural predictions with differentiable binary variables"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "game theoretic interpretability for temporal modeling"
    - "towards robust interpretability with self explaining neural networks"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "answers unite unsupervised metrics for reinforced summarization models"):
  with members:
    - "question answering as an automatic evaluation metric for news article summarization"
    - "improving abstraction in text summarization"
    - "bottom up abstractive summarization"
    - "actor critic based training framework for abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a semantic qa based approach for text summarization evaluation"

Connected through hyperedge HE-5 (induced by source paper "towards annotating and creating sub sentence summary highlights"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adapting the neural encoder decoder framework from single to multi document summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "neural summarization by extracting sentences and words"

Connected through hyperedge HE-6 (induced by source paper "better rewards yield better summaries learning to summarise without references"):
  with members:
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "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"

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)