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: "harnessing the deep web present and future"
  Abstract: Over the past few years, we have built a system that has exposed large volumes of Deep-Web content to Google.com users. The content that our system exposes contributes to more than 1000 search queries per-second and spans over 50 languages and hundreds of domains. The Deep Web has long been acknowledged to be a major source of structured data on the web, and hence accessing Deep-Web content has long been a problem of interest in the data management community. In this paper, we report on where we believe the Deep Web provides value and where it does not. We contrast two very different approaches to exposing Deep-Web content -- the surfacing approach that we used, and the virtual integration approach that has often been pursued in the data management literature. We emphasize where the values of each of the two approaches lie and caution against potential pitfalls. We outline important areas of future research and, in particular, emphasize the value that can be derived from analyzing large collections of potentially disparate structured data on the web.

Connected through hyperedge HE-1 (induced by source paper "exploring the intersections of web science and accessibility"):
  with members:
    - "adapting the hypercube model to archive deferred representations and their descendants"
    - "archiving deferred representations using a two tiered crawling approach"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "an architectural style for ajax"

Connected through hyperedge HE-2 (induced by source paper "recommending related tables"):
  with members:
    - "ad hoc table retrieval using semantic similarity"
    - "entitables smart assistance for entity focused tables"
    - "neural programmer inducing latent programs with gradient descent"

Connected through hyperedge HE-3 (induced by source paper "ad hoc table retrieval using semantic similarity"):
  with members:
    - "entitables smart assistance for entity focused tables"
    - "design patterns for fusion based object retrieval"
    - "word entity duet representations for document ranking"
    - "scientific table search using keyword queries"
    - "modelling user preferences using word embeddings for context aware venue recommendation"
    - "a dual embedding space model for document ranking"
    - "neural enquirer learning to query tables with natural language"
    - "line large scale information network embedding"

Connected through hyperedge HE-4 (induced by source paper "entitables smart assistance for entity focused tables"):
  with members:
    - "neural enquirer learning to query tables with natural language"

Connected through hyperedge HE-5 (induced by source paper "an analysis of structured data on the web"):
  with members:
    - "automatic wrappers for large scale web extraction"

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.DB(Databases)