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: "cutting off redundant repeating generations for neural abstractive summarization"
  Abstract: This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.

Connected through hyperedge HE-1 (induced by source paper "mass masked sequence to sequence pre training for language generation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised statistical machine translation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "double path networks for sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "paperrobot incremental draft generation of scientific ideas"):
  with members:
    - "sql to text generation with graph to sequence model"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "learning to generate wikipedia summaries for underserved languages from wikidata"
    - "describing a knowledge base"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "hierarchical neural story generation"
    - "entity aware image caption generation"
    - "neural text generation a practical guide"

Connected through hyperedge HE-3 (induced by source paper "a large scale multi length headline corpus for improving length constrained headline generation model evaluation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "positional encoding to control output sequence length"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "sequential copying networks"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "positional encoding to control output sequence length"):
  with members:
    - "direct output connection for a high rank language model"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "controllable abstractive summarization"
    - "controlling output length in neural encoder decoders"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-5 (induced by source paper "a novel ilp framework for summarizing content with high lexical variety"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "automatic summarization of student course feedback"
    - "an improved phrase based approach to annotating and summarizing student course responses"

Connected through hyperedge HE-6 (induced by source paper "modeling local dependence in natural language with multi channel recurrent neural networks"):
  with members:
    - "structured attention networks"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "sentence wise smooth regularization for sequence to sequence learning"):
  with members:
    - "frage frequency agnostic word representation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"
    - "dense information flow for neural machine translation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-8 (induced by source paper "abstract meaning representation for multi document summarization"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "structure infused copy mechanisms for abstractive summarization"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "selective encoding for abstractive sentence summarization"
    - "amr parsing using stack lstms"
    - "get to the point summarization with pointer generator networks"
    - "amr to text generation with synchronous node replacement grammar"
    - "robust incremental neural semantic graph parsing"

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)