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: "enhanced precision through multiple reads for ldpc decoding in flash memories"
  Abstract: Multiple reads of the same Flash memory cell with distinct word-line voltages provide enhanced precision for LDPC decoding. In this paper, the word-line voltages are optimized by maximizing the mutual information (MI) of the quantized channel. The enhanced precision from a few additional reads allows FER performance to approach that of full-precision soft information and enables an LDPC code to significantly outperform a BCH code. A constant-ratio constraint provides a significant simplification in the optimization with no noticeable loss in performance. For a well-designed LDPC code, the quantization that maximizes the mutual information also minimizes the frame error rate in our simulations. However, for an example LDPC code with a high error floor caused by small absorbing sets, the MMI quantization does not provide the lowest frame error rate. The best quantization in this case introduces more erasures than would be optimal for the channel MI in order to mitigate the absorbing sets of the poorly designed code. The paper also identifies a trade-off in LDPC code design when decoding is performed with multiple precision levels; the best code at one level of precision will typically not be the best code at a different level of precision.

Connected through hyperedge HE-1 (induced by source paper "a combinatorial methodology for optimizing non binary graph based codes theoretical analysis and applications in data storage"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "spatially coupled code design for partial response channels optimal object minimization approach"
    - "a generalized algebraic approach to optimizing sc ldpc codes"
    - "high performance non binary spatially coupled codes for flash memories"
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "iterative decoding of ldpc codes over the q ary partial erasure channel"

Connected through hyperedge HE-2 (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"
    - "histogram based flash channel estimation"
    - "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information"

Connected through hyperedge HE-3 (induced by source paper "dynamic programming for sequential deterministic quantization of discrete memoryless channels"):
  with members:
    - "mutual information maximizing quantized belief propagation decoding of ldpc codes"
    - "information theoretic bounds based channel quantization design for emerging memories"
    - "how to construct polar codes"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "error characterization mitigation and recovery in flash memory based solid state drives"):
  with members:
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "using ecc dram to adaptively increase memory capacity"
    - "understanding and improving the latency of dram based memory systems"
    - "rowhammer js a remote software induced fault attack in javascript"
    - "improving dram performance by parallelizing refreshes with accesses"

Connected through hyperedge HE-5 (induced by source paper "errors in flash memory based solid state drives analysis mitigation and recovery"):
  with members:
    - "another flip in the wall of rowhammer defenses"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"
    - "understanding and improving the latency of dram based memory systems"

Connected through hyperedge HE-6 (induced by source paper "using dynamic allocation of write voltage to extend flash memory lifetime"):
  with members:
    - "histogram based flash channel estimation"
    - "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information"
    - "constructions of rank modulation codes"

Connected through hyperedge HE-7 (induced by source paper "performance of multilevel flash memories with different binary labelings a multi user perspective"):
  with members:
    - "additive entropies of partitions"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-8 (induced by source paper "histogram based flash channel estimation"):
  with members:
    - "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information"

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)