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: "parallelizing query optimization on shared nothing architectures"
  Abstract: Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We show how to parallelize query optimization at a massive scale. #R##N#We present algorithms for parallel query optimization in left-deep and bushy plan spaces. At optimization start, we divide the plan space for a given query into partitions of equal size that are explored in parallel by worker nodes. At the end of optimization, each worker returns the optimal plan in its partition to the master which determines the globally optimal plan from the partition-optimal plans. No synchronization or data exchange is required during the actual optimization phase. The amount of data sent over the network, at the start and at the end of optimization, as well as the complexity of serial steps within our algorithms increase only linearly in the number of workers and in the query size. The time and space complexity of optimization within one partition decreases uniformly in the number of workers. We parallelize single- and multi-objective query optimization over a cluster with 100 nodes in our experiments, using more than 250 concurrent worker threads (Spark executors). Despite high network latency and task assignment overheads, parallelization yields speedups of up to one order of magnitude for large queries whose optimization takes minutes on a single node.

Connected through hyperedge HE-1 (induced by source paper "solving the join ordering problem via mixed integer linear programming"):
  with members:
    - "skew in parallel query processing"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)