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: "exchange of limits why iterative decoding works"
  Abstract: We consider communication over binary-input memoryless output-symmetric channels using low-density parity-check codes and message-passing decoding. The asymptotic (in the length) performance of such a combination for a fixed number of iterations is given by density evolution. Letting the number of iterations tend to infinity we get the density evolution threshold, the largest channel parameter so that the bit error probability tends to zero as a function of the iterations. #R##N#In practice we often work with short codes and perform a large number of iterations. It is therefore interesting to consider what happens if in the standard analysis we exchange the order in which the blocklength and the number of iterations diverge to infinity. In particular, we can ask whether both limits give the same threshold. #R##N#Although empirical observations strongly suggest that the exchange of limits is valid for all channel parameters, we limit our discussion to channel parameters below the density evolution threshold. Specifically, we show that under some suitable technical conditions the bit error probability vanishes below the density evolution threshold regardless of how the limit is taken.

Connected through hyperedge HE-1 (induced by source paper "scaling exponent of list decoders with applications to polar codes"):
  with members:
    - "finite length scaling for polar codes"
    - "improved bounds on the finite length scaling of polar codes"
    - "on the correlation between polarized becs"
    - "rate dependent analysis of the asymptotic behavior of channel polarization"
    - "universal bounds on the scaling behavior of polar codes"
    - "list decoding of polar codes"
    - "information spectrum approach to second order coding rate in channel coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "hierarchies of local optimality characterizations in decoding of tanner codes"):
  with members:
    - "convergence of weighted min sum decoding via dynamic programming on trees"
    - "on decoding irregular tanner codes with local optimality guarantees"

Connected through hyperedge HE-3 (induced by source paper "strong secrecy on the binary erasure wiretap channel using large girth ldpc codes"):
  with members:
    - "performance analysis and design of two edge type ldpc codes for the bec wiretap channel"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "strong secrecy for erasure wiretap channels"
    - "secure nested codes for type ii wiretap channels"
    - "applications of ldpc codes to the wiretap channel"

Connected through hyperedge HE-4 (induced by source paper "strong secrecy for erasure wiretap channels"):
  with members:
    - "secure nested codes for type ii wiretap channels"
    - "applications of ldpc codes to the wiretap channel"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-5 (induced by source paper "decay of correlations for sparse graph error correcting codes"):
  with members:
    - "decay of correlations in low density parity check codes low noise regime"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "tight bounds for ldpc and ldgm codes under map 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)