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: "reinforced extractive summarization with question focused rewards"
  Abstract: We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive goldstandard labels for extraction units. However, the labels are often inaccurate, because human abstracts and source documents cannot be easily aligned at the word level. In this paper we convert human abstracts to a set of Cloze-style comprehension questions. System summaries are encouraged to preserve salient source content useful for answering questions and share common words with the abstracts. We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries. Our experiments show that the proposed method is effective. It surpasses state-of-the-art systems on the standard summarization dataset.

Connected through hyperedge HE-1 (induced by source paper "guiding extractive summarization with question answering rewards"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"

Connected through hyperedge HE-2 (induced by source paper "searching for effective neural extractive summarization what works and what s next"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "finding generalizable evidence by learning to convince q a models"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "unsupervised pseudo labeling for extractive summarization on electronic health records"):
  with members:
    - "learning to write notes in electronic health records"
    - "neural summarization by extracting sentences and words"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)