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 "how to write summaries with patterns learning towards abstractive summarization through prototype editing"):
  Abstract: Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

Includes members:
  Member "gsn a graph structured network for multi party dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flexner a flexible lstm cnn stack framework for named entity recognition")
  Member "abstractive text summarization by incorporating reader comments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")
  Member "skeleton to response dialogue generation guided by retrieval memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to memorize in neural task oriented dialogue systems")
  Member "newsroom a dataset of 1 3 million summaries with diverse extractive strategies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modelling latent skills for multitask language generation")
  Member "a unified model for extractive and abstractive summarization using inconsistency loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "learning to write with cooperative discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "response generation by context aware prototype editing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relevance promoting language model for short text conversation")
  Member "abstractive summarization of reddit posts with multi level memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")

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)