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: "question answering as an automatic evaluation metric for news article summarization"
  Abstract: Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end-to-end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results.

Connected through hyperedge HE-1 (induced by source paper "ask to learn a study on curiosity driven question generation"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "analyzing uncertainty in neural machine translation"
    - "towards a better metric for evaluating question generation systems"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "finding generalizable evidence by learning to convince q a models"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "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 "answers unite unsupervised metrics for reinforced summarization models"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "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"

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)