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 "on oracle type local recovery guarantees in compressed sensing"):
  Abstract: We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on 1 minimization encompasses the case where (i) the measurements are block-structured samples in order to reflect the structured acquisition that is often encountered in applications; (ii) where the signal has an arbitrary structured sparsity, by results depending on its support S. Within this framework and under a random sign assumption, the number of measurements needed by 1 minimization can be shown to be of the same order than the one required by an oracle least-squares estimator. Moreover, these bounds can be minimized by adapting the variable density sampling to a given prior on the signal support and to the coherence of the measurements. We illustrate both numerically and analytically that our results can be successfully applied to recover Haar wavelet coefficients that are sparse in levels from random Fourier measurements in dimension one and two, which can be of particular interest in imaging problems. Finally, a preliminary numerical investigation shows the potential of this theory for devising adaptive sampling strategies in sparse polynomial approximation.

Includes members:
  Member "compressed sensing and parallel acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class")
  Member "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "compressed sensing with structured sparsity and structured acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "an algorithm for variable density sampling with block constrained acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "signal reconstruction from interferometric measurements under sensing constraints")
  Member "stable and robust sampling strategies for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")

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)