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: "rex recursive delta based data centric computation"
  Abstract: In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. #R##N#Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. #R##N#We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.

Connected through hyperedge HE-1 (induced by source paper "change actions from incremental computation to discrete derivatives"):
  with members:
    - "the differential calculus of causal functions"
    - "change actions models of generalised differentiation"
    - "differentiable causal computations via delayed trace"
    - "evolving the incremental λ calculus into a model of forward automatic differentiation ad"
    - "mackey complete spaces and power series a topological model of differential linear logic"
    - "a convenient differential category"
    - "a computational definition of the notion of vectorial space"

Connected through hyperedge HE-2 (induced by source paper "efficient iterative processing in the scidb parallel array engine"):
  with members:
    - "spinning fast iterative data flows"
    - "riot i o efficient numerical computing without sql"

Connected through hyperedge HE-3 (induced by source paper "linview incremental view maintenance for complex analytical queries"):
  with members:
    - "the madlib analytics library or mad skills the sql"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"
    - "riot i o efficient numerical computing without sql"

Connected through hyperedge HE-4 (induced by source paper "pregelix big ger graph analytics on a dataflow engine"):
  with members:
    - "scaling datalog for machine learning on big data"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "spinning fast iterative data flows"

Connected through hyperedge HE-5 (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"
    - "provenance for aggregate queries"
    - "conditioning probabilistic databases"
    - "objective criteria for the evaluation of clustering methods"

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)