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: "an mse based ttransfer chart to analyze iterative decoding schemes"
  Abstract: An alternative to extrinsic information transfer (EXIT) charts called mean squared error (MSE) charts that use a measure related to the MSE instead of mutual information is proposed. Using the relationship between mutual information and minimum mean squared error (MMSE), a relationship between the rate of any code and the area under a plot of MSE versus signal to noise ratio (SNR) is obtained, when the log likelihood ratios (LLR) can be assumed to be from a Gaussian channel. Using this result, a theoretical justification is provided for designing concatenated codes by matching the EXIT charts of the inner and outer decoders, when the LLRs are Gaussian which is typically assumed for code design using EXIT charts. Finally, for the special case of AWGN channel it is shown that any capacity achieving code has an EXIT curve that is flat. This extends Ashikhmin et als results for erasure channels to the Gaussian channel.

Connected through hyperedge HE-1 (induced by source paper "the generalized area theorem and some of its consequences"):
  with members:
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "improved bounds on the parity check density and achievable rates of binary linear block codes with applications to ldpc codes"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "life above threshold from list decoding to area theorem and mse"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "why we can not surpass capacity the matching condition"):
  with members:
    - "life above threshold from list decoding to area theorem and mse"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "mutual information and minimum mean square error in gaussian channels"):
  with members:
    - "randomly spread cdma asymptotics via statistical physics"
    - "life above threshold from list decoding to area theorem and mse"
    - "shannon meets wiener ii on mmse estimation in successive decoding schemes"

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)