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: "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
  Abstract: We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question "What is the article about?". We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.

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 "generating representative headlines for news stories"):
  with members:
    - "highres highlight based reference less evaluation of summarization"
    - "ernie enhanced representation through knowledge integration"
    - "facet aware evaluation for extractive text summarization"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "ernie enhanced language representation with informative entities"
    - "self attentive model for headline generation"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "unilmv2 pseudo masked language models for unified language model 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"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (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"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "structure infused copy mechanisms for abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-5 (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-6 (induced by source paper "attractive or faithful popularity reinforced learning for inspired headline generation"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "helping news editors write better headlines a recommender to improve the keyword contents shareability of news headlines"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural summarization by extracting sentences and words"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "fairseq a fast extensible toolkit for sequence modeling"):
  with members:
    - "strategies for structuring story generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "self attention with relative position representations"
    - "bottom up abstractive summarization"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

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)