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 "attend to the beginning a study on using bidirectional attention for extractive summarization"):
  Abstract: Forum discussion data differ in both structure and properties from generic form of textual data such as news. Henceforth, summarization techniques should, in turn, make use of such differences, and craft models that can benefit from the structural nature of discussion data. In this work, we propose attending to the beginning of a document, to improve the performance of extractive summarization models when applied to forum discussion data. Evaluations demonstrated that with the help of bidirectional attention mechanism, attending to the beginning of a document (initial comment/post) in a discussion thread, can introduce a consistent boost in ROUGE scores, as well as introducing a new State Of The Art (SOTA) ROUGE scores on the forum discussions dataset. Additionally, we explored whether this hypothesis is extendable to other generic forms of textual data. We make use of the tendency of introducing important information early in the text, by attending to the first few sentences in generic textual data. Evaluations demonstrated that attending to introductory sentences using bidirectional attention, improves the performance of extractive summarization models when even applied to more generic form of textual data.

Includes members:
  Member "extractive summarization of long documents by combining global and local context":
    also connected through other hyperedges:
      (none)
  Member "biset bi directional selective encoding with template for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization")
  Member "text summarization with pretrained encoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "an embarrassingly simple approach for transfer learning from pretrained language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "an improved phrase based approach to annotating and summarizing student course responses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel ilp framework for summarizing content with high lexical variety")

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)