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: "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
  Abstract: Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. This paper studies a $K \times N$ partial Fourier measurement matrix for compressed sensing which is deterministically constructed via cyclic difference sets (CDS). Precisely, the matrix is constructed by $K$ rows of the $N\times N$ inverse discrete Fourier transform (IDFT) matrix, where each row index is from a $(N, K, \lambda)$ cyclic difference set. The restricted isometry property (RIP) is statistically studied for the deterministic matrix to guarantee the recovery of sparse signals. A computationally efficient reconstruction algorithm is then proposed from the structure of the matrix. Numerical results show that the reconstruction algorithm presents competitive recovery performance with allowable computational complexity.

Connected through hyperedge HE-1 (induced by source paper "tolerant compressed sensing with partially coherent sensing matrices"):
  with members:
    - "can we allow linear dependencies in the dictionary in the sparse synthesis framework"
    - "super resolution from noisy data"
    - "sub nyquist radar via doppler focusing"
    - "analysis of block omp using block rip"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "fast correlation computation method for matching pursuit algorithms in compressed sensing"):
  with members:
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "deterministic compressed sensing matrices from multiplicative character sequences"):
  with members:
    - "reed muller sensing matrices and the lasso"
    - "deterministic compressed sensing matrices from additive character sequences"
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"

Connected through hyperedge HE-4 (induced by source paper "deterministic compressed sensing matrices from additive character sequences"):
  with members:
    - "reed muller sensing matrices and the lasso"
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"

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)