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 "a novel ilp framework for summarizing content with high lexical variety"):
  Abstract: Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.

Includes members:
  Member "deep communicating agents for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "newsroom a dataset of 1 3 million summaries with diverse extractive strategies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling latent skills for multitask language generation")
  Member "scientific document summarization via citation contextualization and scientific discourse":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks")
  Member "toward extractive summarization of online forum discussions via hierarchical attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization")
  Member "ranking sentences for extractive summarization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning by semantic similarity makes abstractive summarization better")
  Member "soft layer specific multi task summarization with entailment and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "automatic summarization of student course feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")
  Member "an improved phrase based approach to annotating and summarizing student course responses":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization")

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)