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: "histogram based flash channel estimation"
  Abstract: Current generation Flash devices experience significant read-channel degradation from damage to the oxide layer during program and erase operations. Information about the read-channel degradation drives advanced signal processing methods in Flash to mitigate its effect. In this context, channel estimation must be ongoing since channel degradation evolves over time and is a function of the number of program/erase (P/E) cycles. This paper proposes a framework for on-line model-based channel estimation using limited channel measurements (reads). This paper uses a channel model characterizing degradation as a function of retention time and the amount of charge programmed and erased. For channel histogram measurements, equal-probability (equal-height) bin placement yields a good approximation to the original distribution using only ten bins (i.e. nine reads). With the channel model and binning strategy in place, this paper explores candidate numerical least squares channel estimation algorithms and ultimately demonstrates the effectiveness of the Levenberg-Marquardt algorithm, which provides both speed and accuracy, in an algorithm for voltage allocation.

Connected through hyperedge HE-1 (induced by source paper "deep learning aided dynamic read thresholds design for multi level cell flash memories"):
  with members:
    - "dynamic programming for discrete memoryless channel quantization"
    - "information theoretic bounds based channel quantization design for emerging memories"
    - "an introduction to deep learning for the physical layer"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "on deep learning based channel decoding"
    - "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information"
    - "enhanced precision through multiple reads for ldpc decoding in flash memories"

Connected through hyperedge HE-2 (induced by source paper "using dynamic allocation of write voltage to extend flash memory lifetime"):
  with members:
    - "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information"
    - "enhanced precision through multiple reads for ldpc decoding in flash memories"
    - "constructions of rank modulation codes"

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)