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: "vat asymptotic cost analysis for multi level key value stores"
  Abstract: Over the past years, there has been an increasing number of key-value (KV) store designs, each optimizing for a different set of requirements. Furthermore, with the advancements of storage technology the design space of KV stores has become even more complex. More recent KV-store designs target fast storage devices, such as SSDs and NVM. Most of these designs aim to reduce amplification during data reorganization by taking advantage of device characteristics. However, until today most analysis of KV-store designs is experimental and limited to specific design points. This makes it difficult to compare tradeoffs across different designs, find optimal configurations and guide future KV-store design. In this paper, we introduce the Variable Amplification- Throughput analysis (VAT) to calculate insert-path amplification and its impact on multi-level KV-store performance.We use VAT to express the behavior of several existing design points and to explore tradeoffs that are not possible or easy to measure experimentally. VAT indicates that by inserting randomness in the insert-path, KV stores can reduce amplification by more than 10x for fast storage devices. Techniques, such as key-value separation and tiering compaction, reduce amplification by 10x and 5x, respectively. Additionally, VAT predicts that the advancements in device technology towards NVM, reduces the benefits from both using key-value separation and tiering.

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)