human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "assessing the factual accuracy of generated text"):
  Abstract: We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study.

Includes members:
  Member "tensor2tensor for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "generating wikipedia by summarizing long sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "adafactor adaptive learning rates with sublinear memory cost":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "deep active learning for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "adversarial learning for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non autoregressive neural dialogue generation")
  Member "a deep reinforcement learning chatbot":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a human like open domain chatbot")
  Member "faithful to the original fact aware neural abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph")

Question: Please tell me which class the source paper 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)