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 "optimizing organizations for navigating data lakes"):
  Abstract: Navigation is known to be an effective complement to search. In addition to data discovery, navigation can help users develop a conceptual model of what types of data are available. In data lakes, there has been considerable research on dataset discovery using search. We consider the complementary problem of creating an effective navigation structure over a data lake. We define an organization as a navigation structure (graph) containing nodes representing sets of attributes within a data lake. An edge represents a subset relationship. We propose a novel problem, the data lake organization problem where the goal is to find an organization that allows a user to most efficiently find attributes. We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding an attribute using the organization. Our approach uses attribute values and metadata (when available). For data lakes with little or no metadata, we propose a way of creating metadata using metadata available in other lakes. We propose an approximate algorithm for the organization problem and show its effectiveness on a synthetic benchmark. Finally, we construct an organization on attributes of a real data lake containing data from federal Open Data portals and show that the organization improves the expected probability of discovering attributes over a baseline. Using a second real data lake with no metadata, we show how metadata can be inferred that is effective in enabling organization creation.

Includes members:
  Member "lsh ensemble internet scale domain search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "answering table queries on the web using column keywords":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "web table extraction retrieval and augmentation a survey")

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.DB(Databases)