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 of verification based decoding on the q ary symmetric channel for large q"
  Abstract: We discuss and analyze a list-message-passing decoder with verification for low-density parity-check (LDPC) codes on the q-ary symmetric channel (q-SC). Rather than passing messages consisting of symbol probabilities, this decoder passes lists of possible symbols and marks some lists as verified. The density evolution (DE) equations for this decoder are derived and used to compute decoding thresholds. If the maximum list size is unbounded, then we find that any capacity-achieving LDPC code for the binary erasure channel can be used to achieve capacity on the q-SC for large q. The decoding thresholds are also computed via DE for the case where each list is truncated to satisfy a maximum list size constraint. Simulation results are also presented to confirm the DE results. During the simulations, we observed differences between two verification-based decoding algorithms, introduced by Luby and Mitzenmacher, that were implicitly assumed to be identical. In this paper, we provide an analysis of the node-based algorithms from that paper and verify that it matches simulation results. The probability of false verification (FV) is also considered and techniques are discussed to mitigate the FV. Optimization of the degree distribution is also used to improve the threshold for a fixed maximum list size. Finally, the proposed algorithm is compared with a variety of other algorithms using both density evolution thresholds and simulation results.

Connected through hyperedge HE-1 (induced by source paper "symbol message passing decoding of nonbinary low density parity check codes"):
  with members:
    - "protograph based ldpc code design for ternary message passing decoding"
    - "bounds on the error probability of raptor codes under maximum likelihood decoding"
    - "analysis and design of binary message passing decoders"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing with incremental sparse measurements"):
  with members:
    - "ldpc codes for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "analysis and design of irregular graphs for node based verification based recovery algorithms in compressed sensing"):
  with members:
    - "compressive identification of linear operators"
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-4 (induced by source paper "an efficient approach toward the asymptotic analysis of node based recovery algorithms in compressed sensing"):
  with members:
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing ii analysis and validation"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "compressive sensing using low density frames"

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)