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: "channel models for multi level cell flash memories based on empirical error analysis"
  Abstract: We propose binary discrete parametric channel models for multi-level cell (MLC) flash memories that provide accurate error-correcting code (ECC) performance estimation by modeling the empirically observed error characteristics under program/erase cycling stress. Through a detailed empirical error characterization of 1X-nm and 2Y-nm MLC flash memory chips from two different vendors, we observe and characterize the overdispersion phenomenon in the number of bit errors per ECC frame. A well-studied channel model, such as the binary asymmetric channel model, is unable to provide accurate ECC performance estimation. Hence, we propose a channel model based on the beta-binomial probability distribution [2-beta-binomial (2-BBM) channel model], which is a good fit for the overdispersed empirical error characteristics, and show through statistical tests and simulation results for BCH, low density parity check, and polar codes, that the 2-BBM channel model provides accurate ECC performance estimation in MLC flash memories.

Connected through hyperedge HE-1 (induced by source paper "model based detector for ssds in the presence of inter cell interference"):
  with members:
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "architectural techniques for improving nand flash memory reliability"):
  with members:
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "another flip in the wall of rowhammer defenses"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "using ecc dram to adaptively increase memory capacity"
    - "rowhammer js a remote software induced fault attack in javascript"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of the beta binomial channel model for multi level cell flash memories"):
  with members:
    - "on the capacity of multilevel nand flash memory channels"
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"
    - "achieving the capacity of any dmc using only polar codes"
    - "how to construct polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input 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)