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 "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  Abstract: The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks. The approach measures redundancy between a pair of sentences based on surface form and semantic information. We show that our DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets. Our findings are particularly meaningful for summarizing documents created by multiple authors containing redundant yet lexically diverse expressions.

Includes members:
  Member "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple and effective paraphrastic similarity from parallel translations")
  Member "scoring sentence singletons and pairs for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint parsing and generation for abstractive summarization")
  Member "deep communicating agents for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "improving abstraction in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "avgout a simple output probability measure to eliminate dull responses")
  Member "content selection in deep learning models of summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "toward extractive summarization of online forum discussions via hierarchical attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "learning by semantic similarity makes abstractive summarization better")

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)