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: "abstractive multi document summarization via phrase selection and merging"
  Abstract: We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.

Connected through hyperedge HE-1 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "hierarchical transformers for multi document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "structured neural summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "towards dynamic computation graphs via sparse latent structure"
    - "bottom up abstractive summarization"
    - "automatic detection of vague words and sentences in privacy policies"

Connected through hyperedge HE-3 (induced by source paper "scoring sentence singletons and pairs for abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "structured attention networks"
    - "learning structured text representations"
    - "get to the point summarization with pointer generator networks"
    - "neural discourse structure for text categorization"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "concept pointer network for abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "toward abstractive summarization using semantic representations"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "sequential copying networks"
    - "controlling decoding for more abstractive summaries with copy based networks"

Connected through hyperedge HE-6 (induced by source paper "discofuse a large scale dataset for discourse based sentence fusion"):
  with members:
    - "training tips for the transformer model"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "attention is all you need"
    - "automatic prediction of discourse connectives"
    - "implicit discourse relation classification via multi task neural networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "dependency based text graphs for keyphrase and summary extraction with applications to interactive content retrieval"):
  with members:
    - "text summarization techniques a brief survey"
    - "deep keyphrase generation"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-8 (induced by source paper "adapting the neural encoder decoder framework from single to multi document summarization"):
  with members:
    - "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"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "automatic summarization of student course feedback"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "structure infused copy mechanisms for abstractive 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)