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: "a rate splitting approach to fading channels with imperfect channel state information"
  Abstract: As shown by Medard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input    \(X\)    with power    \(P\)    and by upper-bounding the conditional entropy    \(h(X|Y,\hat {H})\)    by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating    \(X\)    from    \((Y,\hat {H})\)   . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input    \(X\)    as the sum of two independent Gaussian variables    \(X_{1}\)    and    \(X_{2}\)    and by applying Medard's lower bound first to bound the mutual information between    \(X_{1}\)    and    \(Y\)    while treating    \(X_{2}\)    as noise, and by applying it a second time to the mutual information between    \(X_{2}\)    and    \(Y\)    while assuming    \(X_{1}\)    to be known, we obtain a capacity lower bound that is strictly larger than Medard's lower bound. We then generalize this approach to an arbitrary number    \(L\)    of layers, where    \(X\)    is expressed as the sum of    \(L\)    independent Gaussian random variables of respective variances    \(P_\ell \)   ,    \(\ell = 1,\dotsc ,L\)    summing up to    \(P\)   . Among all such rate-splitting bounds, we determine the supremum over power allocations    \(P_\ell \)    and total number of layers    \(L\)   . This supremum is achieved for    \(L \rightarrow \infty \)    and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error    \(H-\hat {H}\)    tends to zero as the SNR tends to infinity.

Connected through hyperedge HE-1 (induced by source paper "a simple capacity lower bound for communication with superimposed pilots"):
  with members:
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "nearest neighbour decoding and pilot aided channel estimation in stationary gaussian flat fading channels"
    - "what is the value of joint processing of pilots and data in block fading channels"
    - "the noncoherent rician fading channel part i structure of the capacity achieving input"

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)