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: "neural network based abstract generation for opinions and arguments"
  Abstract: We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.

Connected through hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "informative and controllable opinion summarization"):
  with members:
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "hierarchical transformers for multi document summarization"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "improving abstraction in text summarization"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "exploring the role of prior beliefs for argument persuasion"
    - "neural argument generation augmented with externally retrieved evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "argument mining with structured svms and rnns"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-5 (induced by source paper "abstractive summarization of reddit posts with multi level memory networks"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "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"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "robust neural abstractive summarization systems and evaluation against adversarial information"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "get to the point summarization with pointer generator networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "faithful to the original fact aware neural abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning for neural keyphrase generation"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "deep keyphrase generation"
    - "semi supervised multitask learning for sequence labeling"
    - "get to the point summarization with pointer generator networks"
    - "empower sequence labeling with task aware neural language model"
    - "language to logical form with neural attention"
    - "unsupervised pretraining for sequence to sequence learning"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-8 (induced by source paper "neural argument generation augmented with externally retrieved evidence"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument mining with structured svms and rnns"
    - "understanding and detecting supporting arguments of diverse types"
    - "sequence to sequence learning as beam search optimization"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

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)