human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximate lifted inference with probabilistic databases"):
  Abstract: This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME self-join-free conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers.

Includes members:
  Member "linearized and single pass belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "oblivious bounds on the probability of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized lineage aware temporal windows supporting outer and anti joins in temporal probabilistic databases")
  Member "uncertainty in crowd data sourcing under structural constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "top k querying of unknown values under order constraints")
  Member "skolemization for weighted first order model counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain liftability of relational marginal polytopes")
  Member "dissociation and propagation for efficient query evaluation over probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "oblivious bounds on the probability of boolean functions")
  Member "probabilistic databases with markoviews":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "uniform reliability of self join free conjunctive queries")
  Member "formula based probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks")
  Member "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "state space abstractions for probabilistic inference a systematic review")

Question: Please tell me which class the source paper 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)