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: "analysis and design of finite alphabet iterative decoders robust to faulty hardware"
  Abstract: This paper addresses the problem of designing low-density parity check decoders robust to transient errors introduced by faulty hardware. We assume that the faulty hardware introduces errors during the message-passing updates, and we propose a general framework for the definition of the message update faulty functions. Within this framework, we define symmetry conditions for the faulty functions and derive two simple error models used in the analysis. With this analysis, we propose a new interpretation of the functional density evolution threshold introduced by Kameni  et al.  in the recent literature and show its limitations in the case of highly unreliable hardware. However, we show that under restricted decoder noise conditions, the functional threshold can be used to predict the convergence behavior of finite alphabet iterative decoders (FAIDs) under faulty hardware. In particular, we reveal the existence of robust and nonrobust FAIDs and propose a framework for the design of robust decoders. We finally illustrate robust- and nonrobust-decoder behaviors of finite-length codes using Monte Carlo simulations.

Connected through hyperedge HE-1 (induced by source paper "faulty successive cancellation decoding of polar codes for the binary erasure channel"):
  with members:
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "on the correlation between polarized becs"
    - "polar codes robustness of the successive cancellation decoder with respect to quantization"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "performance of ldpc codes under faulty iterative decoding"

Connected through hyperedge HE-2 (induced by source paper "performance of ldpc decoders with missing connections"):
  with members:
    - "performance analysis based on density evolution on fault erasure belief propagation decoder"
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "noise facilitation in associative memories of exponential capacity"
    - "limit on the addressability of fault tolerant nanowire decoders"
    - "performance of ldpc codes under faulty iterative decoding"
    - "iterative decoding of low density parity check codes a survey"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-3 (induced by source paper "modeling and energy optimization of ldpc decoder circuits with timing violations"):
  with members:
    - "on the total power capacity of regular ldpc codes with iterative message passing decoders"
    - "on the energy complexity of ldpc decoder circuits"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "state dependent statistical timing model for voltage scaled circuits"
    - "energy consumption of vlsi decoders"
    - "performance of ldpc codes under faulty iterative decoding"

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)