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: "uncertainty in crowd data sourcing under structural constraints"
  Abstract: Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are expected to minimize this uncertainty relative to the overall data collection goal. Such problems are already challenging when we assume that questions are unrelated and answers are independent, but they are even more complicated when we assume that the unknown values follow hard structural constraints (such as monotonicity).

Connected through hyperedge HE-1 (induced by source paper "top k querying of unknown values under order constraints"):
  with members:
    - "possible and certain answers for queries over order incomplete data"
    - "a fast and practical method to estimate volumes of convex polytopes"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "human assisted graph search it s okay to ask questions"
    - "a unified approach to ranking in probabilistic databases"
    - "semantics and evaluation of top k queries in probabilistic databases"

Connected through hyperedge HE-2 (induced by source paper "structurally tractable uncertain data"):
  with members:
    - "declarative statistical modeling with datalog"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "probabilistic databases with markoviews"

Connected through hyperedge HE-3 (induced by source paper "approximate lifted inference with probabilistic databases"):
  with members:
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "skolemization for weighted first order model counting"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "dissociation and propagation for approximate lifted inference with standard relational database management systems"):
  with members:
    - "approximate lifted inference with probabilistic databases"
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "skolemization for weighted first order model counting"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"
    - "node splitting a scheme for generating upper bounds in bayesian networks"

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)