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 "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"):
  Abstract: We study the problem of sparse reconstruction from noisy undersampled measurements when the following knowledge is available. (1) We are given partial, and partly erroneous, knowledge of the signal's support, denoted by T . (2) We are also given an erroneous estimate of the signal values on T, denoted by (μ)T . In practice, both of these may be available from prior knowledge. Alternatively, in recursive reconstruction applications, like real-time dynamic MRI, one can use the support estimate and the signal value estimate from the previous time instant as T and (μ)T. In this paper, we introduce regularized modified basis pursuit denoising (BPDN) (reg-mod-BPDN) to solve this problem and obtain computable bounds on its reconstruction error. Reg-mod-BPDN tries to find the signal that is sparsest outside the set T, while being “close enough” to (μ)T on T and while satisfying the data constraint. Corresponding results for modified-BPDN and BPDN follow as direct corollaries. A second key contribution is an approach to obtain computable error bounds that hold without any sufficient conditions. This makes it easy to compare the bounds for the various approaches. Empirical reconstruction error comparisons with many existing approaches are also provided.

Includes members:
  Member "reprocs a missing link between recursive robust pca and recursive sparse recovery in large but correlated noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset")
  Member "compressive imaging using approximate message passing and a markov tree prior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity optimality of amp in coded systems")
  Member "ls cs residual ls cs compressive sensing on least squares residual":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recovery of signals under the high order rip condition via prior support information")
  Member "kalman filtered compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "designing recurrent neural networks by unfolding an l1 l1 minimization algorithm")

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)