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: "iterative decoding of low density parity check codes a survey"
  Abstract: Much progress has been made on decoding algorithms for error-correcting codes in the last decade. In this article, we give an introduction to some fundamental results on iterative, message-passing algorithms for low-density parity check codes. For certain important stochastic channels, this line of work has enabled getting very close to Shannon capacity with algorithms that are extremely efficient (both in theory and practice).

Connected through hyperedge HE-1 (induced by source paper "ldpc codes achieve list decoding capacity"):
  with members:
    - "improved list decodability of random linear binary codes"
    - "improved decoding of folded reed solomon and multiplicity codes"
    - "linear time list recovery of high rate expander codes"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "folded codes from function field towers and improved optimal rate list 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"
    - "analysis and design of finite alphabet iterative decoders robust to faulty hardware"
    - "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"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-3 (induced by source paper "semi fragile image authentication based on cfd and 3 bit quantization"):
  with members:
    - "subjective and objective quality assessment of image a survey"

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)