human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "novel entity discovery from web tables"):
  Abstract: When working with any sort of knowledge base (KB) one has to make sure it is as complete and also as up-to-date as possible. Both tasks are non-trivial as they require recall-oriented efforts to determine which entities and relationships are missing from the KB. As such they require a significant amount of labor. Tables on the Web, on the other hand, are abundant and have the distinct potential to assist with these tasks. In particular, we can leverage the content in such tables to discover new entities, properties, and relationships. Because web tables typically only contain raw textual content we first need to determine which cells refer to which known entities---a task we dub table-to-KB matching. This first task aims to infer table semantics by linking table cells and heading columns to elements of a KB. Then second task builds upon these linked entities and properties to not only identify novel ones in the same table but also to bootstrap their type and additional relationships. We refer to this process as novel entity discovery and, to the best of our knowledge, it is the first endeavor on mining the unlinked cells in web tables. Our method identifies not only out-of-KB (``novel'') information but also novel aliases for in-KB (``known'') entities. When evaluated using three purpose-built test collections, we find that our proposed approaches obtain a marked improvement in terms of precision over our baselines whilst keeping recall stable.

Includes members:
  Member "auto completion for data cells in relational tables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "matchzoo a learning practicing and developing system for neural text matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "end to end neural entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning cross context entity representations from text")
  Member "ad hoc table retrieval using semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "improving entity linking by modeling latent relations between mentions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "on the fly table generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "web table extraction retrieval and augmentation a survey")
  Member "deep joint entity disambiguation with local neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "the birth of collective memories analyzing emerging entities in text streams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "early discovery of emerging entities in microblogs")

Question: Please tell me which class the source paper 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)