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 "deep learning aided dynamic read thresholds design for multi level cell flash memories"):
  Abstract: The practical NAND flash memory suffers from various non-stationary noises that are difficult to be predicted. Furthermore, the data retention noise induced channel offset is unknown during the readback process. This severely affects the data recovery from the memory cell. In this paper, we first propose a novel recurrent neural network (RNN)-based detector to effectively detect the data symbols stored in the multi-level-cell (MLC) flash memory without any prior knowledge of the channel. However, compared with the conventional threshold detector, the proposed RNN detector introduces much longer read latency and more power consumption. To tackle this problem, we further propose an RNN-aided (RNNA) dynamic threshold detector, whose detection thresholds can be derived based on the outputs of the RNN detector. We thus only need to activate the RNN detector periodically when the system is idle. Moreover, to enable soft-decision decoding of error-correction codes, we first show how to obtain more read thresholds based on the hard-decision read thresholds derived from the RNN detector. We then propose integer-based reliability mappings based on the designed read thresholds, which can generate the soft information of the channel. Finally, we propose to apply density evolution (DE) combined with differential evolution algorithm to optimize the read thresholds for LDPC coded flash memory channels. Computer simulation results demonstrate the effectiveness of our RNNA dynamic read thresholds design, for both the uncoded and LDPC-coded flash memory channels, without any prior knowledge of the channel.

Includes members:
  Member "dynamic programming for discrete memoryless channel quantization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "entropy constrained maximizing mutual information quantization")
  Member "information theoretic bounds based channel quantization design for emerging memories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic programming for sequential deterministic quantization of discrete memoryless channels")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "error characterization mitigation and recovery in flash memory based solid state drives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "on deep learning based channel decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks")
  Member "histogram based flash channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using dynamic allocation of write voltage to extend flash memory lifetime")
  Member "increasing flash memory lifetime by dynamic voltage allocation for constant mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using dynamic allocation of write voltage to extend flash memory lifetime")
  Member "enhanced precision through multiple reads for ldpc decoding in flash memories":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)