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 "robustness of sparse recovery via f minimization a topological viewpoint"):
  Abstract: A recent trend in compressed sensing is to consider nonconvex optimization techniques for sparse recovery. The important case of    $F$   -minimization has become of particular interest, for which the exact reconstruction condition (ERC) in the noiseless setting can be precisely characterized by the null space property (NSP). However, little work has been done concerning its robust reconstruction condition (RRC) in the noisy setting. We look at the null space of the measurement matrix as a point on the Grassmann manifold, and then study the relation between the ERC and RRC sets, denoted as    $\Omega _{J}$    and    $\Omega _{J}^{r}$   , respectively. It is shown that    $\Omega _{J}^{r}$    is the interior of    $\Omega _{J}$   , from which a previous result of the equivalence of ERC and RRC for    $\ell _{p}$   -minimization follows easily as a special case. Moreover, when    $F$    is nondecreasing, it is shown that    $\overline {\Omega }_{J}\setminus \mathop {\mathrm {int\kern 0pt}} (\Omega _{J})$    is a set of measure zero and of the first category. As a consequence, the probabilities of ERC and RRC are the same if the measurement matrix    $\mathbf {A}$    is randomly generated according to a continuous distribution. Quantitatively, if the null space    $\mathcal {N}( A)$    lies in the    $d$   -interior of    $\Omega _{J}$   , then RRC will be satisfied with the robustness constant    $C={2+2d}/{d\sigma _{\min }(\mathbf {A}^{\top })}$   ; and conversely, if RRC holds with    $C={2-2d}/{d\sigma _{\max }(\mathbf {A}^{\top })}$   , then    $\mathcal {N}( A)$    must lie in    $d$   -interior of    $\Omega _{J}$   . We also present several rules for comparing the performances of different cost functions. Finally, these results are capitalized to derive achievable tradeoffs between the measurement rate and robustness with the aid of Gordon’s escape through the mesh theorem or a connection between NSP and the restricted eigenvalue condition.

Includes members:
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "approximate projected generalized gradient methods with sparsity inducing penalties":
    also connected through other hyperedges:
      (none)
  Member "the convergence guarantees of a non convex approach for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast and provable admm for learning with generative priors")
  Member "proof of convergence and performance analysis for sparse recovery via zero point attracting projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an improved variable step size zero point attracting projection algorithm")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "on the stable recovery of the sparsest overcomplete representations in presence of noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "successive concave sparsity approximation for compressed sensing")

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)