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: "consensus answers for queries over probabilistic databases"
  Abstract: We address the problem of finding a "best" deterministic query answer to a query over a probabilistic database. For this purpose, we propose the notion of a consensus world (or a consensus answer) which is a deterministic world (answer) that minimizes the expected distance to the possible worlds (answers). This problem can be seen as a generalization of the well-studied inconsistent information aggregation problems (e.g. rank aggregation) to probabilistic databases. We consider this problem for various types of queries including SPJ queries, \Topk queries, group-by aggregate queries, and clustering. For different distance metrics, we obtain polynomial time optimal or approximation algorithms for computing the consensus answers (or prove NP-hardness). Most of our results are for a general probabilistic database model, called {\em and/xor tree model}, which significantly generalizes previous probabilistic database models like x-tuples and block-independent disjoint models, and is of independent interest.

Connected through hyperedge HE-1 (induced by source paper "store location selection via mining search query logs of baidu maps"):
  with members:
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-2 (induced by source paper "consistency checking and querying in probabilistic databases under integrity constraints"):
  with members:
    - "probabilistic description logics"
    - "conditioning probabilistic databases"
    - "the dichotomy of conjunctive queries on probabilistic structures"
    - "probabilistic deduction with conditional constraints over basic events"

Connected through hyperedge HE-3 (induced by source paper "maximizing expected utility for stochastic combinatorial optimization problems"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "when lp is the cure for your matching woes approximating stochastic matchings"
    - "a unified approach to ranking in probabilistic databases"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"
    - "stochastic combinatorial optimization under probabilistic constraints"

Connected through hyperedge HE-4 (induced by source paper "a unified approach to ranking in probabilistic databases"):
  with members:
    - "optimal junction trees"
    - "ranking under uncertainty"
    - "ranking with submodular valuations"
    - "conditioning probabilistic databases"

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)