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: "extractive summarization limits compression generalized model and heuristics"
  Abstract: Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.

Connected through hyperedge HE-1 (induced by source paper "leafnats an open source toolkit and live demo system for neural abstractive text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "deep reinforcement learning for sequence to sequence models"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "xnmt the extensible neural machine translation toolkit"
    - "bottom up abstractive summarization"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "newswire versus social media for disaster response and recovery"):
  with members:
    - "graph based semi supervised learning with convolution neural networks to classify crisis related tweets"
    - "domain adaptation with adversarial training and graph embeddings"
    - "linking tweets with monolingual and cross lingual news using transformed word embeddings"
    - "be in the know connecting news articles to relevant twitter conversations"
    - "an analysis of twitter messages in the 2011 tohoku earthquake"

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)