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: "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.

Connected through hyperedge HE-1 (induced by source paper "generalized lineage aware temporal windows supporting outer and anti joins in temporal probabilistic databases"):
  with members:
    - "oblivious bounds on the probability of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "lineage aware temporal windows supporting set operations in temporal probabilistic databases"):
  with members:
    - "oblivious bounds on the probability of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "verdictdb universalizing approximate query processing"):
  with members:
    - "database learning toward a database that becomes smarter every time"
    - "visualization aware sampling for very large databases"
    - "a sampling algebra for aggregate estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "the big data bootstrap"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-4 (induced by source paper "database learning toward a database that becomes smarter every time"):
  with members:
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"
    - "interactive data exploration with smart drill down"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-5 (induced by source paper "tractable lineages on treelike instances limits and extensions"):
  with members:
    - "provenance circuits for trees and treelike instances extended version"
    - "polynomial bounds for the grid minor theorem"
    - "complexity of inference in graphical models"
    - "lower bounds for the complexity of monadic second order logic"
    - "are there any good digraph width measures"
    - "algorithmic meta theorems"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-6 (induced by source paper "dissociation and propagation for approximate lifted inference with standard relational database management systems"):
  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"
    - "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)