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: "almost strong consistency good enough in distributed storage systems"
  Abstract: A consistency/latency tradeoff arises as soon as a distributed storage system replicates data. For low latency, modern storage systems often settle for weak consistency conditions, which provide little, or even worse, no guarantee for data consistency. In this paper we propose the notion of almost strong consistency as a better balance option for the consistency/latency tradeoff. It provides both deterministically bounded staleness of data versions for each read and probabilistic quantification on the rate of "reading stale values", while achieving low latency. In the context of distributed storage systems, we investigate almost strong consistency in terms of 2-atomicity. Our 2AM (2-Atomicity Maintenance) algorithm completes both reads and writes in one communication round-trip, and guarantees that each read obtains the value of within the latest 2 versions. To quantify the rate of "reading stale values", we decompose the so-called "old-new inversion" phenomenon into concurrency patterns and read-write patterns, and propose a stochastic queueing model and a "timed balls-into-bins model" to analyze them, respectively. The theoretical analysis not only demonstrates that "old-new inversions" rarely occur as expected, but also reveals that the read-write pattern dominates in guaranteeing such rare data inconsistencies. These are further confirmed by the experimental results, showing that 2-atomicity is "good enough" in distributed storage systems by achieving low latency, bounded staleness, and rare data inconsistencies.

Connected through hyperedge HE-1 (induced by source paper "characterizing and adapting the consistency latency tradeoff in distributed key value stores"):
  with members:
    - "continuous partial quorums for consistency latency tuning in distributed nosql storage systems"
    - "probabilistically bounded staleness for practical partial quorums"

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)