human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "biset bi directional selective encoding with template for abstractive summarization"
  Abstract: The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset were conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.

Connected through hyperedge HE-1 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization"):
  with members:
    - "extractive summarization of long documents by combining global and local context"
    - "text summarization with pretrained encoders"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an improved phrase based approach to annotating and summarizing student course responses"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-2 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "sample efficient text summarization using a single pre trained transformer"
    - "bertscore evaluating text generation with bert"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"

Connected through hyperedge HE-3 (induced by source paper "a large scale multi length headline corpus for improving length constrained headline generation model evaluation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "positional encoding to control output sequence length"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "sequential copying networks"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Question: Please tell me which class the center node 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)