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: "bigpatent a large scale dataset for abstractive and coherent summarization"
  Abstract: Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article's global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.

Connected through hyperedge HE-1 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "evaluating the factual consistency of abstractive text summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "pretraining based natural language generation for text summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "neural text summarization a critical evaluation"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"

Connected through hyperedge HE-3 (induced by source paper "in conclusion not repetition comprehensive abstractive summarization with diversified attention based on determinantal point processes"):
  with members:
    - "representation degeneration problem in training natural language generation models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "bottom up abstractive summarization"
    - "wikihow a large scale text summarization dataset"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "fast greedy map inference for determinantal point process to improve recommendation diversity"

Connected through hyperedge HE-4 (induced by source paper "on extractive and abstractive neural document summarization with transformer language models"):
  with members:
    - "jointly extracting and compressing documents with summary state representations"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "controlling decoding for more abstractive summaries with copy based networks"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "classify or select neural architectures for extractive document summarization"

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)