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: "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"
  Abstract: Compressed Sensing aims to capture attributes of a sparse signal using very few measurements. Cand\`{e}s and Tao showed that sparse reconstruction is possible if the sensing matrix acts as a near isometry on all $\boldsymbol{k}$-sparse signals. This property holds with overwhelming probability if the entries of the matrix are generated by an iid Gaussian or Bernoulli process. There has been significant recent interest in an alternative signal processing framework; exploiting deterministic sensing matrices that with overwhelming probability act as a near isometry on $\boldsymbol{k}$-sparse vectors with uniformly random support, a geometric condition that is called the Statistical Restricted Isometry Property or StRIP. This paper considers a family of deterministic sensing matrices satisfying the StRIP that are based on \srm codes (binary chirps) and a $\boldsymbol{k}$-sparse reconstruction algorithm with sublinear complexity. In the presence of stochastic noise in the data domain, this paper derives bounds on the $\boldsymbol{\ell_2}$ accuracy of approximation in terms of the $\boldsymbol{\ell_2}$ norm of the measurement noise and the accuracy of the best $\boldsymbol{k}$-sparse approximation, also measured in the $\boldsymbol{\ell_2}$ norm. This type of $\boldsymbol{\ell_2 /\ell_2}$ bound is tighter than the standard $\boldsymbol{\ell_2 /\ell_1}$ or $\boldsymbol{\ell_1/ \ell_1}$ bounds.

Connected through hyperedge HE-1 (induced by source paper "state of the art and prospects of structured sensing matrices in compressed sensing"):
  with members:
    - "convolutional compressed sensing using deterministic sequences"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "structured compressed sensing from theory to applications"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "deterministic compressed sensing matrices from multiplicative character sequences"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "deterministic compressed sensing matrices from multiplicative character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "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"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"

Connected through hyperedge HE-3 (induced by source paper "deterministic compressed sensing matrices from additive character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "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"

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)