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: "towards real time summarization of scheduled events from twitter streams"
  Abstract: This paper explores the real-time summarization of scheduled events such as soccer games from torrential flows of Twitter streams. We propose and evaluate an approach that substantially shrinks the stream of tweets in real-time, and consists of two steps: (i) sub-event detection, which determines if something new has occurred, and (ii) tweet selection, which picks a representative tweet to describe each sub-event. We compare the summaries generated in three languages for all the soccer games in "Copa America 2011" to reference live reports offered by Yahoo! Sports journalists. We show that simple text analysis methods which do not involve external knowledge lead to summaries that cover 84% of the sub-events on average, and 100% of key types of sub-events (such as goals in soccer). Our approach should be straightforwardly applicable to other kinds of scheduled events such as other sports, award ceremonies, keynote talks, TV shows, etc.

Connected through hyperedge HE-1 (induced by source paper "intweetive text summarization"):
  with members:
    - "artex is another text summarizer"
    - "automatic summarization system coupled with a question answering system qaas"

Connected through hyperedge HE-2 (induced by source paper "sub event detection from twitter streams as a sequence labeling problem"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "an attentive neural architecture for joint segmentation and parsing and its application to real estate ads"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "convolutional neural networks for sentence classification"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-3 (induced by source paper "mining social media for newsgathering a review"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "fake news detection on social media a data mining perspective"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "hoaxy a platform for tracking online misinformation"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "sub story detection in twitter with hierarchical dirichlet processes"):
  with members:
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "multiscale event detection in social media"

Connected through hyperedge HE-5 (induced by source paper "a relevant content filtering based framework for data stream summarization"):
  with members:
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-6 (induced by source paper "the early bird catches the term combining twitter and news data for event detection and situational awareness"):
  with members:
    - "early stage influenza detection from twitter"

Connected through hyperedge HE-7 (induced by source paper "real time classification of twitter trends"):
  with members:
    - "trends in social media persistence and decay"

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)