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 "compressed sensing with prior information optimal strategies geometry and bounds"):
  Abstract: We address the problem of compressed sensing (CS) with prior information: reconstruct a target CS signal with the aid of a similar signal that is known beforehand, our prior information. We integrate the additional knowledge of the similar signal into CS via L1-L1 and L1-L2 minimization. We then establish bounds on the number of measurements required by these problems to successfully reconstruct the original signal. Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, L1-L1 minimization improves the performance of CS dramatically. In contrast, L1-L2 minimization has a performance very similar to classical CS and brings no significant benefits. All our findings are illustrated with experimental results.

Includes members:
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "robust analysis l1 recovery from gaussian measurements and total variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "corrupted sensing novel guarantees for separating structured signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "off the grid spectral compressed sensing with prior information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed compressed sensing off the grid")
  Member "sharp mse bounds for proximal denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "simultaneously structured models with application to sparse and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)