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: "parallel combining benefits of explicit synchronization"
  Abstract: Parallel batched data structures are designed to process synchronized batches of operations in a parallel computing model. In this paper, we propose parallel combining, a technique that implements a concurrent data structure from a parallel batched one. The idea is that we explicitly synchronize concurrent operations into batches: one of the processes becomes a combiner which collects concurrent requests and initiates a parallel batched algorithm involving the owners (clients) of the collected requests. Intuitively, the cost of synchronizing the concurrent calls can be compensated by running the parallel batched algorithm. #R##N#We validate the intuition via two applications of parallel combining. First, we use our technique to design a concurrent data structure optimized for read-dominated workloads, taking a dynamic graph data structure as an example. Second, we use a novel parallel batched priority queue to build a concurrent one. In both cases, we obtain performance gains with respect to the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "parallel finger search structures"):
  with members:
    - "optimal parallel algorithms in the binary forking model"
    - "optimal multithreaded batch parallel 2 3 trees"
    - "parallel working set search structures"
    - "batch parallel euler tour trees"
    - "a general technique for non blocking trees"
    - "fast parallel operations on search trees"
    - "flat combining and the synchronization parallelism tradeoff"

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.DC(Distributed, Parallel, and Cluster Computing)