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: "adapting the neural encoder decoder framework from single to multi document summarization"
  Abstract: Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.

Connected through hyperedge HE-1 (induced by source paper "generating representative headlines for news stories"):
  with members:
    - "highres highlight based reference less evaluation of summarization"
    - "ernie enhanced representation through knowledge integration"
    - "facet aware evaluation for extractive text summarization"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "ernie enhanced language representation with informative entities"
    - "self attentive model for headline generation"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "guiding extractive summarization with question answering rewards"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"

Connected through hyperedge HE-6 (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"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "generating a common question from multiple documents using multi source encoder decoder models"):
  with members:
    - "passage re ranking with bert"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"
    - "input combination strategies for multi source transformer decoder"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-8 (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"

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)