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: "actor critic based training framework for abstractive summarization"
  Abstract: We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this problem, we employ an actor-critic framework to enhance the training procedure. For the actor, we employ the typical attention based sequence-to-sequence (seq2seq) framework as the policy network for summary generation. For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones. Policy gradient method is used to conduct the parameter learning. An alternating training strategy is proposed to conduct the joint training of the actor and critic models. Extensive experiments on some benchmark datasets in different languages show that our framework achieves improvements over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "multi stage pretraining for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "pretraining based natural language generation for text summarization"
    - "neural text generation with unlikelihood training"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "rethinking imagenet pre training"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "persona aware tips generation"):
  with members:
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "contrastive attention mechanism for abstractive sentence summarization"):
  with members:
    - "global encoding for abstractive summarization"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "answers unite unsupervised metrics for reinforced summarization models"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "question answering as an automatic evaluation metric for news article summarization"
    - "improving abstraction in text summarization"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a semantic qa based approach for text summarization evaluation"

Connected through hyperedge HE-7 (induced by source paper "bottom up abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating wikipedia by summarizing long sequences"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "classify or select neural architectures for extractive document summarization"

Connected through hyperedge HE-8 (induced by source paper "sequence generation with guider network"):
  with members:
    - "model ensemble trust region policy optimization"
    - "style transfer from non parallel text by cross alignment"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"
    - "curiosity driven exploration by self supervised prediction"
    - "twin networks matching the future for sequence generation"
    - "a deep reinforced model 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)