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 "towards secure compressive sampling scheme"):
  Abstract: There have been some pioneering works concerning embedding cryptographic properties in Compressive Sampling (CS) but it turns out that the concise linear projection encoding process makes this approach ineffective. Here we introduce a bi-level protection (BLP) model for constructing secure compressive sampling scheme. Then we propose several techniques to establish secret key-related sparsifying basis and deploy them into our new CS model. It is demonstrated that the encoding process is simply a random linear projection, which is the same as the traditional model. However, decoding the measurements requires the knowledge of both the key-related sensing matrix and the key-related sparsifying basis. We apply the proposed model to construct digital image cipher under the parallel compressive sampling reconstruction framework. The main properties of this cipher, such as low computational complexity, compressibility, robustness and computational secrecy under known/chosen plaintext attacks, are thoroughly studied. It is shown that compressive sampling scheme based on our BLP model is robust under various attack scenarios although the encoding process is a simple linear projection.

Includes members:
  Member "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "low complexity multiclass encryption by compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "low complexity cloud image privacy protection via matrix perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bi level protected compressive sampling")
  Member "joint quantization and diffusion for compressed sensing measurements of natural images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bi level protected compressive sampling")
  Member "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "embedding cryptographic features in compressive sensing":
    also connected through other hyperedges:
      (none)
  Member "ripless compressed sensing from anisotropic measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "permutation enhanced parallel reconstruction with a linear compressive sampling device":
    also connected through other hyperedges:
      (none)

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)