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: "sample efficient text summarization using a single pre trained transformer"
  Abstract: Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best use pre-trained LMs for generation tasks such as abstractive summarization, particularly to enhance sample efficiency. In these sequence-to-sequence settings, prior work has experimented with loading pre-trained weights into the encoder and/or decoder networks, but used non-pre-trained encoder-decoder attention weights. We instead use a pre-trained decoder-only network, where the same Transformer LM both encodes the source and generates the summary. This ensures that all parameters in the network, including those governing attention over source states, have been pre-trained before the fine-tuning step. Experiments on the CNN/Daily Mail dataset show that our pre-trained Transformer LM substantially improves over pre-trained Transformer encoder-decoder networks in limited-data settings. For instance, it achieves 13.1 ROUGE-2 using only 1% of the training data (~3000 examples), while pre-trained encoder-decoder models score 2.3 ROUGE-2.

Connected through hyperedge HE-1 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "preference based interactive multi document summarisation"

Connected through hyperedge HE-3 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "autoregressive text generation beyond feedback loops"
    - "deep reinforcement learning for sequence to sequence models"
    - "neural text generation past present and beyond"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"
    - "deep state space models for unconditional word generation"

Connected through hyperedge HE-4 (induced by source paper "leveraging pre trained checkpoints for sequence generation tasks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bert rediscovers the classical nlp pipeline"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "to tune or not to tune how about the best of both worlds"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-5 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "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"
    - "toward abstractive summarization using semantic representations"

Connected through hyperedge HE-6 (induced by source paper "cooperative generator discriminator networks for abstractive summarization with narrative flow"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "discourse aware neural rewards for coherent text generation"

Connected through hyperedge HE-7 (induced by source paper "samsum corpus a human annotated dialogue dataset for abstractive summarization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "pretraining based natural language generation for text summarization"
    - "abstractive summarization of spoken and written conversation"
    - "data driven summarization of scientific articles"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "bottom up abstractive summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

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)