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: "lse precoders for massive mimo with hardware constraints fundamental limits"
  Abstract: This paper proposes the nonlinear Least Square Error (LSE) precoders for multiuser MIMO broadcast channels. The output signals of LSE Precoders are limited to be chosen from a predefined set which let these precoders address several constraints such as peak power limitation, constant envelope transmission and discrete constellations. We study the large-system performance of these precoders via the replica method from statistical physics, and derive a closed-form expression for the asymptotic distortion. Our results demonstrate that an LSE precoder with the output peak-to-average power ratio of $3~{\rm dB}$ can track the performance of the Regularized Zero Forcing (RZF) precoder closely. As the peak-to-average power ratio reduces to one, the constant envelope precoder is recovered. The investigations depict that the performance of the RZF precoder is achieved by the constant envelope precoder with $20\%$ of more transmit antennas. For $M$-PSK constellations, our analysis gives a lower-bound on the asymptotic distortion which is tight for moderate antenna-to-user ratios and deviates as the ratio grows. We improve this bound by deriving the replica solution under one-step of replica symmetry breaking. Our numerical investigations for this case show that the bound is tight for antenna-to-user ratios less than $5$.

Connected through hyperedge HE-1 (induced by source paper "replica symmetry breaking in compressive sensing"):
  with members:
    - "rsb decoupling property of map estimators"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "optimal phase transitions in compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "vector precoding for wireless mimo systems and its replica analysis"

Connected through hyperedge HE-2 (induced by source paper "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"):
  with members:
    - "nonlinear precoders for massive mimo systems with general constraints"
    - "rsb decoupling property of map estimators"
    - "per antenna constant envelope precoding for large multi user mimo systems"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-3 (induced by source paper "statistical mechanics of map estimation general replica ansatz"):
  with members:
    - "glse precoders for massive mimo systems analysis and applications"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "replica symmetry breaking in compressive sensing"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"

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)