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: "compressive identification of linear operators"
  Abstract: We consider the problem of identifying a linear deterministic operator from an input-output measurement. For the large class of continuous (and hence bounded) operators, under additional mild restrictions, we show that stable identifiability is possible if the total support area of the operator's spreading function satisfies Δ ≤ 1/2. This result holds for arbitrary (possibly fragmented) support regions of the spreading function, does not impose limitations on the total extent of the support region, and, most importantly, does not require the support region of the spreading function to be known prior to identification. Furthermore, we prove that asking for identifiability of only almost all operators, stable identifiability is possible if Δ ≤ 1. This result is surprising as it says that there is no penalty for not knowing the support region of the spreading function prior to identification.

Connected through hyperedge HE-1 (induced by source paper "sampling and reconstruction of operators"):
  with members:
    - "cornerstones of sampling of operator theory"
    - "sampling of stochastic operators"
    - "identification of sparse linear operators"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "the restricted isometry property for time frequency structured random matrices"

Connected through hyperedge HE-2 (induced by source paper "identification of sparse linear operators"):
  with members:
    - "subspace methods for joint sparse recovery"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "recovery of sparsely corrupted signals"
    - "rank awareness in joint sparse recovery"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "compressed sensing of analog signals in shift invariant spaces"

Connected through hyperedge HE-3 (induced by source paper "analysis and design of irregular graphs for node based verification based recovery algorithms in compressed sensing"):
  with members:
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "analysis of verification based decoding on the q ary symmetric channel for large q"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)