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: "how much is wikipedia lagging behind news"
  Abstract: Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other knowledge bases are based on extracting entity and event based knowledge from it. Online news, on the other hand, is an authoritative and rich source for emerging entities, events and facts relating to existing entities. In this work, we study the creation of entities in Wikipedia with respect to news by studying how entity and event based information flows from news to Wikipedia.   We analyze the lag of Wikipedia (based on the revision history of the English Wikipedia) with 20 years of The New York Times dataset (NYT). We model and analyze the lag of entities and events, namely their first appearance in Wikipedia and in NYT, respectively. In our extensive experimental analysis, we find that almost 20% of the external references in entity pages are news articles encoding the importance of news to Wikipedia. Second, we observe that the entity-based lag follows a normal distribution with a high standard deviation, whereas the lag for news-based events is typically very low. Finally, we find that events are responsible for creation of emergent entities with as many as 12% of the entities mentioned in the event page are created after the creation of the event page.

Connected through hyperedge HE-1 (induced by source paper "neural based statement classification for biased language"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "finding news citations for wikipedia"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "approaches for enriching and improving textual knowledge bases"):
  with members:
    - "fine grained citation span for references in wikipedia"
    - "finding news citations for wikipedia"
    - "automated news suggestions for populating wikipedia entity pages"

Connected through hyperedge HE-3 (induced by source paper "ongoing events in wikipedia a cross lingual case study"):
  with members:
    - "multiwiki interlingual text passage alignment in wikipedia"

Connected through hyperedge HE-4 (induced by source paper "discovering entities with just a little help from you"):
  with members:
    - "history by diversity helping historians search news archives"

Connected through hyperedge HE-5 (induced by source paper "finding news citations for wikipedia"):
  with members:
    - "automated news suggestions for populating wikipedia entity pages"

Connected through hyperedge HE-6 (induced by source paper "automated news suggestions for populating wikipedia entity pages"):
  with members:
    - "fast and accurate annotation of short texts with wikipedia pages"

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)