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: "projection design for statistical compressive sensing a tight frame based approach"
  Abstract: In this paper, we develop a framework to design sensing matrices for compressive sensing applications that lead to good mean squared error (MSE) performance subject to sensing cost constraints. By capitalizing on the MSE of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we use the fact that a Parseval tight frame is the closest design - in the Frobenius norm sense - to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracleestimator with respect to the equivalent sensing matrix, subject to sensing energy constraints. Based on this result, we then propose two sensing matrix designs that exhibit two key properties: the designs are closed form rather than iterative; the designs exhibit superior performance in relation to other designs in the literature, which is revealed by our numerical investigation in various scenarios with different sparse recovery algorithms including basis pursuit de-noise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP).

Connected through hyperedge HE-1 (induced by source paper "online multilinear dictionary learning"):
  with members:
    - "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"
    - "an efficient method for robust projection matrix design"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "separable dictionary learning"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  with members:
    - "compressive sensing of sparse tensors"
    - "separable cosparse analysis operator learning"
    - "optimized projections for compressed sensing via rank constrained nearest correlation matrix"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing of simultaneous low rank and joint sparse matrices"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing"):
  with members:
    - "on collaborative compressive sensing systems the framework design and algorithm"
    - "global optimality in low rank matrix optimization"
    - "trainlets dictionary learning in high dimensions"
    - "seboost boosting stochastic learning using subspace optimization techniques"
    - "an efficient method for robust projection matrix design"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"

Connected through hyperedge HE-4 (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"
    - "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"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-5 (induced by source paper "modulated unit norm tight frames for compressed sensing"):
  with members:
    - "sparsity averaging for compressive imaging"
    - "convolutional compressed sensing using deterministic sequences"
    - "the restricted isometry property for random block diagonal matrices"
    - "spread spectrum magnetic resonance imaging"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-6 (induced by source paper "measurement matrix design for compressive detection with secrecy guarantees"):
  with members:
    - "distributed inference in the presence of eavesdroppers a survey"
    - "perfect secrecy via compressed sensing"
    - "measurement design for detecting sparse signals"

Connected through hyperedge HE-7 (induced by source paper "structured matching pursuit for reconstruction of dynamic sparse channels"):
  with members:
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "variable earns profit improved adaptive channel estimation using sparse vss nlms algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "greedy sparse signal recovery with tree pruning"):
  with members:
    - "generalized orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing performance bounds under poisson noise"
    - "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)