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: "faster query answering in probabilistic databases using read once functions"
  Abstract: A boolean expression is in read-once form if each of its variables appears exactly once. When the variables denote independent events in a probability space, the probability of the event denoted by the whole expression in read-once form can be computed in polynomial time (whereas the general problem for arbitrary expressions is #P-complete). Known approaches to checking read-once property seem to require putting these expressions in disjunctive normal form. In this paper, we tell a better story for a large subclass of boolean event expressions: those that are generated by conjunctive queries without self-joins and on tuple-independent probabilistic databases. We first show that given a tuple-independent representation and the provenance graph of an SPJ query plan without self-joins, we can, without using the DNF of a result event expression, efficiently compute its co-occurrence graph. From this, the read-once form can already, if it exists, be computed efficiently using existing techniques. Our second and key contribution is a complete, efficient, and simple to implement algorithm for computing the read-once forms (whenever they exist) directly, using a new concept, that of co-table graph, which can be significantly smaller than the co-occurrence graph.

Connected through hyperedge HE-1 (induced by source paper "explaining wrong queries using small examples"):
  with members:
    - "smoke fine grained lineage at interactive speed"
    - "semi supervised verified feedback generation"
    - "data generation for testing and grading sql queries"
    - "on the limitations of provenance for queries with difference"
    - "provenance for aggregate queries"

Connected through hyperedge HE-2 (induced by source paper "provenance circuits for trees and treelike instances extended version"):
  with members:
    - "transducers with origin information"
    - "on the limitations of provenance for queries with difference"

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"
    - "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-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"
    - "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"

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)