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: "phase diagram of matrix compressed sensing"
  Abstract: In the problem of matrix compressed sensing we aim to recover a low-rank matrix from few of its element-wise linear projections. In this contribution we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices. The results that we obtain using the replica method describe the state evolution of the recently introduced P-BiG-AMP algorithm. We show the existence of different types of phase transitions, their implications for the solvability of the problem, and we compare the results of the theoretical analysis to the performance reached by P-BiG-AMP. Remarkably the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse channel estimation for massive mimo with 1 bit feedback per dimension"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-3 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "parametric bilinear generalized approximate message passing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "low rank modeling and its applications in image analysis"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

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)