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: "banditsum extractive summarization as a contextual bandit"
  Abstract: In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.

Connected through hyperedge HE-1 (induced by source paper "length controllable abstractive summarization by guiding with summary prototype"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

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 "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text 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"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-4 (induced by source paper "evaluating the factual consistency of abstractive text summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "pretraining based natural language generation for text summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "neural text summarization a critical evaluation"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"

Connected through hyperedge HE-5 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"

Connected through hyperedge HE-6 (induced by source paper "discourse aware neural extractive model for text summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "searching for effective neural extractive summarization what works and what s next"
    - "an entity driven framework for abstractive summarization"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "hibert document level pre training of hierarchical bidirectional transformers for document summarization"):
  with members:
    - "neural extractive text summarization with syntactic compression"
    - "deep communicating agents for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "iterative document representation learning towards summarization with polishing"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "searching for effective neural extractive summarization what works and what s next"):
  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"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "reinforced extractive summarization with question focused rewards"

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)