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: "neural extractive summarization with side information"
  Abstract: Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor with attention over side information. We evaluate our model on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart that does not use any side information, in terms of both informativeness and fluency.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "ranking sentences from product description bullets for better search"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "towards a simplified ontology for better e commerce search"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"
    - "distraction based neural networks for document summarization"

Connected through hyperedge HE-4 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-5 (induced by source paper "neural related work summarization with a joint context driven attention mechanism"):
  with members:
    - "scientific article summarization using citation context and article s discourse structure"
    - "node2vec scalable feature learning for networks"
    - "neural summarization by extracting sentences and words"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "sequence to sequence rnns for text summarization"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised opinion summarization by leveraging external information"):
  with members:
    - "unsupervised multi document opinion summarization as copycat review generation"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "ranking sentences for extractive summarization with reinforcement learning"):
  with members:
    - "graph based neural multi document summarization"
    - "deep reinforcement learning for dialogue generation"
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "show attend and tell neural image caption generation with visual attention"
    - "character aware neural language models"

Connected through hyperedge HE-8 (induced by source paper "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "hierarchical neural story generation"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "convolutional sequence to sequence learning"
    - "controllable abstractive summarization"
    - "contextual lstm clstm models for large scale nlp tasks"

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)