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: "the simplest solution to an underdetermined system of linear equations"
  Abstract: Consider a d times n matrix A, with d < n. The problem of solving for x in y = Ax is underdetermined, and has infinitely many solutions (if there are any). Given y, the minimum Kolmogorov complexity solution (MKCS) of the input x is defined to be an input z (out of many) with minimum Kolmogorov-complexity that satisfies y = Az. One expects that if the actual input is simple enough, then MKCS will recover the input exactly. This paper presents a preliminary study of the existence and value of the complexity level up to which such a complexity-based recovery is possible. It is shown that for the set of all d times n binary matrices (with entries 0 or 1 and d < n), MKCS exactly recovers the input for an overwhelming fraction of the matrices provided the Kolmogorov complexity of the input is O(d). A weak converse that is loose by a log n factor is also established for this case. Finally, we investigate the difficulty of finding a matrix that has the property of recovering inputs with complexity of O(d) using MKCS

Connected through hyperedge HE-1 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-2 (induced by source paper "approximate message passing with universal denoising"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-3 (induced by source paper "approximate message passing algorithm with universal denoising and gaussian mixture learning"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-4 (induced by source paper "compression based compressed sensing"):
  with members:
    - "universal compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "sharp recovery bounds for convex demixing with applications"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-5 (induced by source paper "rate distortion dimension of stochastic processes"):
  with members:
    - "compression based compressed sensing"
    - "universal compressed sensing"
    - "model based compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing via universal denoising and approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "signal estimation with additive error metrics in compressed sensing"
    - "empirical bayes and full bayes for signal estimation"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "information complexity and estimation"

Connected through hyperedge HE-7 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "stable image reconstruction using total variation minimization"
    - "minimum complexity pursuit stability analysis"
    - "sampling and recovery of pulse streams"
    - "optimal phase transitions in compressed sensing"
    - "minimum complexity pursuit"

Connected through hyperedge HE-8 (induced by source paper "from compression to compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "minimum complexity pursuit stability analysis"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"

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)