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: "bottom up abstractive summarization"
  Abstract: Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.

Connected through hyperedge HE-1 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "knowledge distillation from internal representations"
    - "small and practical bert models for sequence labeling"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "cloze driven pretraining of self attention networks"

Connected through hyperedge HE-2 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "text summarization with pretrained encoders"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "toward abstractive summarization using semantic representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-4 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph"):
  with members:
    - "passage re ranking with bert"
    - "evaluating the factual consistency of abstractive text summarization"
    - "make lead bias in your favor a simple and effective method for news summarization"
    - "assessing the factual accuracy of generated text"
    - "text summarization with pretrained encoders"
    - "unified language model pre training for natural language understanding and generation"
    - "optimizing the factual correctness of a summary a study of summarizing radiology reports"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-5 (induced by source paper "learning by semantic similarity makes abstractive summarization better"):
  with members:
    - "prophetnet predicting future n gram for sequence to sequence pre training"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "summary level training of sentence rewriting for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
    - "better rewards yield better summaries learning to summarise without references"
    - "adversarial text generation via feature mover s distance"

Connected through hyperedge HE-6 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "structure infused copy mechanisms for abstractive summarization"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "selective encoding for abstractive sentence summarization"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "discriminative adversarial search for abstractive summarization"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "answers unite unsupervised metrics for reinforced summarization models"
    - "defending against neural fake news"
    - "bridging the gap between training and inference for neural machine translation"
    - "better rewards yield better summaries learning to summarise without references"
    - "pytext a seamless path from nlp research to production"
    - "analyzing uncertainty in neural machine translation"
    - "language gans falling short"

Connected through hyperedge HE-8 (induced by source paper "attend to the beginning a study on using bidirectional attention for extractive summarization"):
  with members:
    - "extractive summarization of long documents by combining global and local context"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an improved phrase based approach to annotating and summarizing student course responses"
    - "get to the point summarization with pointer generator networks"

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)