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: "a unified approach to ranking in probabilistic databases"
  Abstract: The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions, called PRF-w and PRF-e, that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially PRF-e, at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.

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"
    - "fast and simple relational processing of uncertain data"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-2 (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"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "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"
    - "semantics and evaluation of top k queries in probabilistic databases"

Connected through hyperedge HE-3 (induced by source paper "range queries on uncertain data"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "range counting coresets for uncertain data"

Connected through hyperedge HE-4 (induced by source paper "ranking with diverse intents and correlated contents"):
  with members:
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "a note on the generalized min sum set cover problem"
    - "minimum latency submodular cover"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "ranking with submodular valuations"
    - "dependent randomized rounding for matroid polytopes and applications"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-5 (induced by source paper "language constraint reachability learning in probabilistic graphs"):
  with members:
    - "methods to determine node centrality and clustering in graphs with uncertain structure"

Connected through hyperedge HE-6 (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"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"
    - "consensus answers for queries over probabilistic databases"
    - "stochastic combinatorial optimization under probabilistic constraints"

Connected through hyperedge HE-7 (induced by source paper "provenance for aggregate queries"):
  with members:
    - "the complexity of causality and responsibility for query answers and non answers"
    - "conditioning probabilistic databases"
    - "10 10 6 worlds and beyond efficient representation and processing of incomplete information"

Connected through hyperedge HE-8 (induced by source paper "probabilistic frequent pattern growth for itemset mining in uncertain databases technical report"):
  with members:
    - "fast and simple relational processing of uncertain data"

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)