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: "probabilistic databases with markoviews"
  Abstract: Most of the work on query evaluation in probabilistic databases has focused on the simple tuple-independent data model, where tuples are independent random events. Several efficient query evaluation techniques exists in this setting, such as safe plans, algorithms based on OBDDs, tree-decomposition and a variety of approximation algorithms. However, complex data analytics tasks often require complex correlations, and query evaluation then is significantly more expensive, or more restrictive. In this paper, we propose MVDB as a framework both for representing complex correlations and for efficient query evaluation. An MVDB specifies correlations by views, called MarkoViews, on the probabilistic relations and declaring the weights of the view's outputs. An MVDB is a (very large) Markov Logic Network. We make two sets of contributions. First, we show that query evaluation on an MVDB is equivalent to evaluating a Union of Conjunctive Query(UCQ) over a tuple-independent database. The translation is exact (thus allowing the techniques developed for tuple independent databases to be carried over to MVDB), yet it is novel and quite non-obvious (some resulting probabilities may be negative!). This translation in itself though may not lead to much gain since the translated query gets complicated as we try to capture more correlations. Our second contribution is to propose a new query evaluation strategy that exploits offline compilation to speed up online query evaluation. Here we utilize and extend our prior work on compilation of UCQ. We validate experimentally our techniques on a large probabilistic database with MarkoViews inferred from the DBLP data.

Connected through hyperedge HE-1 (induced by source paper "uniform reliability of self join free conjunctive queries"):
  with members:
    - "the shapley value of tuples in query answering"
    - "quantifying causal effects on query answering in databases"
    - "symmetric weighted first order model counting"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (induced by source paper "research directions for principles of data management dagstuhl perspectives workshop 16151"):
  with members:
    - "size bounds and query plans for relational joins"
    - "a survey on measuring indirect discrimination in machine learning"
    - "query rewriting and optimization for ontological databases"
    - "communication steps for parallel query processing"
    - "the vc dimension of queries and selectivity estimation through sampling"

Connected through hyperedge HE-4 (induced by source paper "learning tuple probabilities"):
  with members:
    - "sum product networks a new deep architecture"
    - "conditioning probabilistic databases"
    - "fast and simple relational processing of uncertain data"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-5 (induced by source paper "structurally tractable uncertain data"):
  with members:
    - "declarative statistical modeling with datalog"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "on the complexity of mining itemsets from the crowd using taxonomies"

Connected through hyperedge HE-6 (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"
    - "formula based probabilistic inference"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-7 (induced by source paper "symmetric weighted first order model counting"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "skolemization for weighted first order model counting"
    - "symmetry aware marginal density estimation"
    - "algebraic model counting"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "understanding the complexity of lifted inference and asymmetric weighted model counting"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "skolemization for weighted first order model counting"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "probabilistic theorem proving"

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)