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: "concentration of measure inequalities for toeplitz matrices with applications"
  Abstract: We derive Concentration of Measure (CoM) inequalities for randomized Toeplitz matrices. These inequalities show that the norm of a high-dimensional signal mapped by a Toeplitz matrix to a low-dimensional space concentrates around its mean with a tail probability bound that decays exponentially in the dimension of the range space divided by a quantity which is a function of the signal. For the class of sparse signals, the introduced quantity is bounded by the sparsity level of the signal. However, we observe that this bound is highly pessimistic for most sparse signals and we show that if a random distribution is imposed on the non-zero entries of the signal, the typical value of the quantity is bounded by a term that scales logarithmically in the ambient dimension. As an application of the CoM inequalities, we consider Compressive Binary Detection (CBD).

Connected through hyperedge HE-1 (induced by source paper "quadrature compressive sampling for radar signals"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "noise folding in compressed sensing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-2 (induced by source paper "technical report observability with random observations"):
  with members:
    - "technical report observability of a linear system under sparsity constraints"
    - "reconstruction of arbitrary biochemical reaction networks a compressive sensing approach"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "observability with random observations"):
  with members:
    - "technical report observability of a linear system under sparsity constraints"
    - "reconstruction of arbitrary biochemical reaction networks a compressive sensing approach"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)