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 "bi level protected compressive sampling"):
  Abstract: Some pioneering works have investigated embedding cryptographic properties in compressive sampling (CS) in a way similar to one-time pad symmetric cipher. This paper tackles the problem of constructing a CS-based symmetric cipher under the key reuse circumstance, i.e., the cipher is resistant to common attacks even when a fixed measurement matrix is used multiple times. To this end, we suggest a bi-level protected CS (BLP-CS) model which makes use of the advantage of measurement matrix construction without restricted isometry property (RIP). Specifically, two kinds of artificial basis mismatch techniques are investigated to construct key-related sparsifying bases. It is demonstrated that the encoding process of BLP-CS is simply a random linear projection, which is the same as the basic CS model. However, decoding the linear measurements requires knowledge of both the key-dependent sensing matrix and its sparsifying basis. The proposed model is exemplified by sampling images as a joint data acquisition and protection layer for resource-limited wireless sensors. Simulation results and numerical analyses have justified that the new model can be applied in circumstances where the measurement matrix can be reused.

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 "towards secure compressive sampling scheme")
  Member "joint quantization and diffusion for compressed sensing measurements of natural images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards secure compressive sampling scheme")
  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 "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 "fast and efficient compressive sensing using structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)