human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "communications inspired projection design with application to compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "measurement matrix design for phase retrieval based on mutual information")
  Member "signal space cosamp for sparse recovery with redundant dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing")
  Member "signal recovery on incoherent manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic aspects of inverse problems using generative models")
  Member "exploiting statistical dependencies in sparse representations for signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting restricted boltzmann machines and deep belief networks in compressed sensing")
  Member "how well can we estimate a sparse vector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "frame moments and welch bound with erasures")

Question: Please tell me which class the source paper 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)