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: "fast and simple relational processing of uncertain data"
  Abstract: This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relation representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.

Connected through hyperedge HE-1 (induced by source paper "uncertainty annotated databases a lightweight approach for approximating certain answers extended version"):
  with members:
    - "mimir bringing ctables into practice"
    - "dissociation and propagation for approximate lifted inference with standard relational database management systems"
    - "a unified approach to ranking in probabilistic databases"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-2 (induced by source paper "learning tuple probabilities"):
  with members:
    - "probabilistic databases with markoviews"
    - "sum product networks a new deep architecture"
    - "conditioning probabilistic databases"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-3 (induced by source paper "managing large scale scientific hypotheses as uncertain and probabilistic data"):
  with members:
    - "upsilon db a system for data driven hypothesis management and analytics"
    - "causality in bayesian belief networks"
    - "causal networks semantics and expressiveness"
    - "a call to arms revisiting database design"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "conditioning probabilistic databases"

Connected through hyperedge HE-4 (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"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "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"

Connected through hyperedge HE-5 (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"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "making massive probabilistic databases practical"):
  with members:
    - "aggregation in probabilistic databases via knowledge compilation"
    - "provenance for aggregate queries"
    - "scalable probabilistic databases with factor graphs and mcmc"

Connected through hyperedge HE-7 (induced by source paper "probabilistic databases with markoviews"):
  with members:
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "scalable probabilistic databases with factor graphs and mcmc"
    - "conditioning probabilistic databases"
    - "a knowledge compilation map"
    - "on the tractable counting of theory models and its application to belief revision and truth maintenance"
    - "the good old davis putnam procedure helps counting models"

Connected through hyperedge HE-8 (induced by source paper "tuffy scaling up statistical inference in markov logic networks using an rdbms"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "new advances in inference by recursive conditioning"

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)