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: "conditioning probabilistic databases"
  Abstract: Past research on probabilistic databases has studied the problem of answering queries on a static database. Application scenarios of probabilistic databases however often involve the conditioning of a database using additional information in the form of new evidence. The conditioning problem is thus to transform a probabilistic database of priors into a posterior probabilistic database which is materialized for subsequent query processing or further refinement. It turns out that the conditioning problem is closely related to the problem of computing exact tuple confidence values. #R##N#It is known that exact confidence computation is an NP-hard problem. This has led researchers to consider approximation techniques for confidence computation. However, neither conditioning nor exact confidence computation can be solved using such techniques. #R##N#In this paper we present efficient techniques for both problems. We study several problem decomposition methods and heuristics that are based on the most successful search techniques from constraint satisfaction, such as the Davis-Putnam algorithm. We complement this with a thorough experimental evaluation of the algorithms proposed. Our experiments show that our exact algorithms scale well to realistic database sizes and can in some scenarios compete with the most efficient previous approximation algorithms.

Connected through hyperedge HE-1 (induced by source paper "infinite probabilistic databases"):
  with members:
    - "on constrained open world probabilistic databases"
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "probabilistic databases with an infinite open world assumption"
    - "aggregation in probabilistic databases via knowledge compilation"
    - "markov logic in infinite domains"

Connected through hyperedge HE-2 (induced by source paper "on optimizing operator fusion plans for large scale machine learning in systemml"):
  with members:
    - "flare native compilation for heterogeneous workloads in apache spark"
    - "weld rethinking the interface between data intensive applications"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "faq questions asked frequently"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-3 (induced by source paper "learning tuple probabilities"):
  with members:
    - "probabilistic databases with markoviews"
    - "sum product networks a new deep architecture"
    - "fast and simple relational processing of uncertain data"
    - "the dichotomy of conjunctive queries on probabilistic structures"

Connected through hyperedge HE-4 (induced by source paper "managing large scale scientific hypotheses as uncertain and probabilistic data"):
  with members:
    - "upsilon db a system for data driven hypothesis management and analytics"
    - "causality in bayesian belief networks"
    - "causal networks semantics and expressiveness"
    - "a call to arms revisiting database design"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "fast and simple relational processing of uncertain data"

Connected through hyperedge HE-5 (induced by source paper "upsilon db managing scientific hypotheses as uncertain data"):
  with members:
    - "a call to arms revisiting database design"

Connected through hyperedge HE-6 (induced by source paper "enframe a platform for processing probabilistic data"):
  with members:
    - "aggregation and ordering in factorised databases"
    - "aggregation in probabilistic databases via knowledge compilation"
    - "uncertain centroid based partitional clustering of uncertain data"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "rex recursive delta based data centric computation"
    - "provenance for aggregate queries"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-7 (induced by source paper "consistency checking and querying in probabilistic databases under integrity constraints"):
  with members:
    - "probabilistic description logics"
    - "consensus answers for queries over probabilistic databases"
    - "the dichotomy of conjunctive queries on probabilistic structures"
    - "probabilistic deduction with conditional constraints over basic events"

Connected through hyperedge HE-8 (induced by source paper "probabilistic databases with markoviews"):
  with members:
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "scalable probabilistic databases with factor graphs and mcmc"
    - "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)