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: "error correction for nor memory devices with exponentially distributed read noise"
  Abstract: The scaling of high density NOR Flash memory devices with multi level cell (MLC) hits the reliability break wall because of relatively high intrinsic bit error rate (IBER). The chip maker companies offer two solutions to meet the output bit error rate (OBER) specification: either partial coverage with error correction code (ECC) or data storage in single level cell (SLC) with significant increase of the die cost. The NOR flash memory allows to write information in small portions, therefore the full error protection becomes costly due to high required redundancy, e.g. $\sim$50%. This is very different from the NAND flash memory writing at once large chunks of information; NAND ECC requires just $\sim$10% redundancy. This paper gives an analysis of a novel error protection scheme applicable to NOR storage of one byte. The method does not require any redundant cells, but assumes 5th program level. The information is mapped to states in the 4-dimensional space separated by the minimal Manhattan distance equal 2. This code preserves the information capacity: one byte occupies four memory cells. We demonstrate the OBER $\sim$ IBER$^{3/2}$ scaling law, where IBER is calculated for the 4-level MLC memory. As an example, the 4-level MLC with IBER $\sim10^{-9}$, which is unacceptable for high density products, can be converted to OBER $\sim10^{-12}$. We assume that the IBER is determined by the exponentially distributed read noise. This is the case for NOR Flash memory devices, since the exponential tails are typical for the random telegraph signal (RTS) noise and for most of the charge loss, charge gain, and charge sharing data losses.

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)