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: "multi document abstractive summarization using ilp based multi sentence compression"
  Abstract: Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.

Connected through hyperedge HE-1 (induced by source paper "earlier isn t always better sub aspect analysis on corpus and system biases in summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "a simple theoretical model of importance for summarization"
    - "toward abstractive summarization using semantic representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "a dataset of peer reviews peerread collection insights and nlp applications"

Connected through hyperedge HE-2 (induced by source paper "generating summaries tailored to target characteristics"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-3 (induced by source paper "dial2desc end to end dialogue description generation"):
  with members:
    - "unsupervised abstractive meeting summarization with multi sentence compression and budgeted submodular maximization"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "image captioning with semantic attention"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "densely connected convolutional networks"
    - "guided open vocabulary image captioning with constrained beam search"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised abstractive sentence summarization using length controlled variational autoencoder"):
  with members:
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a neural attention model for abstractive sentence summarization"
    - "sequence to sequence learning with neural networks"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (induced by source paper "essmart way to manage user requests"):
  with members:
    - "what do support analysts know about their customers on the study and prediction of support ticket escalations in large software organizations"
    - "text summarization techniques a brief survey"
    - "ecrits visualizing support ticket escalation risk"

Connected through hyperedge HE-6 (induced by source paper "enumeration of extractive oracle summaries"):
  with members:
    - "abstractive multi document summarization via phrase selection and merging"
    - "learning mixtures of submodular shells with application to document summarization"
    - "large margin learning of submodular summarization methods"

Connected through hyperedge HE-7 (induced by source paper "summarizing situational and topical information during crises"):
  with members:
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "processing social media messages in mass emergency a survey"

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)