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: "tgsum build tweet guided multi document summarization dataset"
  Abstract: The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions. We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.

Connected through hyperedge HE-1 (induced by source paper "gamewikisum a novel large multi document summarization dataset"):
  with members:
    - "learning to create sentence semantic relation graphs for multi document summarization"
    - "generating wikipedia by summarizing long sequences"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-2 (induced by source paper "causal effects of brevity on style and success in social media"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "a computational approach to politeness with application to social factors"
    - "exploring text virality in social networks"
    - "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"

Connected through hyperedge HE-3 (induced by source paper "ranking sentences for extractive summarization with reinforcement learning"):
  with members:
    - "graph based neural multi document summarization"
    - "neural extractive summarization with side information"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "live blog corpus for summarization"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-5 (induced by source paper "cqasumm building references for community question answering summarization corpora"):
  with members:
    - "lexrank graph based lexical centrality as salience in text summarization"

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.IR(Information Retrieval)