human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning aided dynamic read thresholds design for multi level cell flash memories")
  Member "enhanced precision through multiple reads for ldpc decoding in flash memories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage")

Question: Please tell me which class the source paper 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)