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: "dissociation and propagation for efficient query evaluation over probabilistic databases"
  Abstract: Probabilistic inference over large data sets is a challenging data management problem as exact inference is generally #P-hard and requires sampling-based methods. This paper proposes an alternative approach for approximate evaluation of 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 PTIME self-join-free conjunctive queries: A query is in PTIME if and only if our algorithm returns one single plan. Furthermore, our approach is a generalization of a family of efficient ranking functions from graphs to hypergraphs. We also apply three relational query optimization techniques to evaluate all minimal 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. We also note that the techniques developed in this paper apply immediately to lifted inference from statistical relational models since lifted inference corresponds to PTIME plans in probabilistic databases.

Connected through hyperedge HE-1 (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"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "oblivious bounds on the probability of boolean functions"):
  with members:
    - "dissociation and propagation for approximate lifted inference with standard relational database management systems"
    - "structured message passing"
    - "approximation by quantization"
    - "formula based probabilistic inference"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "optimal upper and lower bounds for boolean expressions by dissociation"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (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"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"
    - "node splitting a scheme for generating upper bounds in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "probabilistic databases with markoviews"):
  with members:
    - "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"
    - "fast and simple relational processing of uncertain data"
    - "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"

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)