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: "resolution aware network coded storage"
  Abstract: In this paper, we show that coding can be used in storage area networks (SANs) to improve various quality of service metrics under normal SAN operating conditions, without requiring additional storage space. For our analysis, we develop a model which captures modern characteristics such as constrained I/O access bandwidth limitations. Using this model, we consider two important cases: single-resolution (SR) and multi-resolution (MR) systems. For SR systems, we use blocking probability as the quality of service metric and propose the network coded storage (NCS) scheme as a way to reduce blocking probability. The NCS scheme codes across file chunks in time, exploiting file striping and file duplication. Under our assumptions, we illustrate cases where SR NCS provides an order of magnitude savings in blocking probability. For MR systems, we introduce saturation probability as a quality of service metric to manage multiple user types, and we propose the uncoded resolution- aware storage (URS) and coded resolution-aware storage (CRS) schemes as ways to reduce saturation probability. In MR URS, we align our MR layout strategy with traffic requirements. In MR CRS, we code videos across MR layers. Under our assumptions, we illustrate that URS can in some cases provide an order of magnitude gain in saturation probability over classic non-resolution aware systems. Further, we illustrate that CRS provides additional saturation probability savings over URS.

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.IT(Information Theory)