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: "information theoretic bounds based channel quantization design for emerging memories"
  Abstract: Channel output quantization plays a vital role in high-speed emerging memories such as the spin-torque transfer magnetic random access memory (STT-MRAM), where high-precision analog-to-digital converters (ADCs) are not applicable. In this paper, we investigate the design of the 1-bit quantizer which is highly suitable for practical applications. We first propose a quantized channel model for STT-MRAM. We then analyze various information theoretic bounds for the quantized channel, including the channel capacity, cutoff rate, and the Polyanskiy-Poor-Verd\'{u} (PPV) finite-length performance bound. By using these channel measurements as criteria, we design and optimize the 1-bit quantizer numerically for the STT-MRAM channel. Simulation results show that the proposed quantizers significantly outperform the conventional minimum mean-squared error (MMSE) based Lloyd-Max quantizer, and can approach the performance of the 1-bit quantizer optimized by error rate simulations.

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"
    - "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"
    - "enhanced precision through multiple reads for ldpc decoding in flash memories"

Connected through hyperedge HE-2 (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"
    - "enhanced precision through multiple reads for ldpc decoding in flash memories"
    - "how to construct polar codes"
    - "quantization of binary input discrete memoryless channels"

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

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)