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: "circulant and toeplitz matrices in compressed sensing"
  Abstract: Compressed sensing seeks to recover a sparse vector from a small number of linear and non-adaptive measurements. While most work so far focuses on Gaussian or Bernoulli random measurements we investigate the use of partial random circulant and Toeplitz matrices in connection with recovery by $\ell_1$-minization. In contrast to recent work in this direction we allow the use of an arbitrary subset of rows of a circulant and Toeplitz matrix. Our recovery result predicts that the necessary number of measurements to ensure sparse reconstruction by $\ell_1$-minimization with random partial circulant or Toeplitz matrices scales linearly in the sparsity up to a $\log$-factor in the ambient dimension. This represents a significant improvement over previous recovery results for such matrices. As a main tool for the proofs we use a new version of the non-commutative Khintchine inequality.

Connected through hyperedge HE-1 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications"):
  with members:
    - "matrix infinitely divisible series tail inequalities and applications in optimization"
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "a note on element wise matrix sparsification via a matrix valued bernstein inequality"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "deterministic partial binary circulant compressed sensing matrices"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "convolutional compressed sensing using deterministic sequences"
    - "restricted isometries for partial random circulant matrices"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "on learning with shift invariant structures"):
  with members:
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "dictionary learning for adaptive gpr target classification"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "compressive shift retrieval"
    - "shift invariance sparse coding for audio classification"

Connected through hyperedge HE-4 (induced by source paper "one bit compressed sensing with partial gaussian circulant matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "on the gap between rip properties and sparse recovery conditions"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "multi view in lensless compressive imaging"
    - "wireless compressive sensing for energy harvesting sensor nodes"

Connected through hyperedge HE-6 (induced by source paper "improved bounds for sparse recovery from subsampled random convolutions"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "the restricted isometry property of subsampled fourier matrices"
    - "fast and rip optimal transforms"
    - "the restricted isometry property for time frequency structured random matrices"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-7 (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-8 (induced by source paper "symmetric toeplitz structured compressed sensing matrices"):
  with members:
    - "mixed compressed sensing based on random graphs"
    - "compressed sensing based on random symmetric bernoulli matrix"
    - "the restricted isometry property for time frequency structured random matrices"
    - "restricted isometries for partial random circulant matrices"

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)