human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
  Abstract: Traditional compressed sensing considers sampling a 1D signal. For a multidimensional signal, if reshaped into a vector, the required size of the sensing matrix becomes dramatically large, which increases the storage and computational complexity significantly. To solve this problem, the multidimensional signal is reshaped into a 2D signal, which is then sampled and reconstructed column by column using the same sensing matrix. This approach is referred to as parallel compressed sensing, and it has much lower storage and computational complexity. For a given reconstruction performance of parallel compressed sensing, if a so-called acceptable permutation is applied to the 2D signal, the corresponding sensing matrix is shown to have a smaller required order of restricted isometry property condition, and thus, lower storage and computation complexity at the decoder are required. A zigzag-scan-based permutation is shown to be particularly useful for signals satisfying the newly introduced layer model. As an application of the parallel compressed sensing with the zigzag-scan-based permutation, a video compression scheme is presented. It is shown that the zigzag-scan-based permutation increases the peak signal-to-noise ratio of reconstructed images and video frames.

Connected through hyperedge HE-1 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices"):
  with members:
    - "indistinguishability and energy sensitivity of asymptotically gaussian compressed encryption"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "bi level protected compressive sampling"
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "a compressed sensing wire tap channel"

Connected through hyperedge HE-2 (induced by source paper "bi level protected compressive sampling"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "ripless compressed sensing from anisotropic measurements"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a probabilistic and ripless theory of compressed sensing"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "towards secure compressive sampling scheme"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "embedding cryptographic features in compressive sensing"
    - "ripless compressed sensing from anisotropic measurements"
    - "permutation enhanced parallel reconstruction with a linear compressive sampling device"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "embedding cryptographic features in compressive sensing"):
  with members:
    - "low complexity multiclass encryption by compressed sensing"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "on compression of data encrypted with block ciphers"

Connected through hyperedge HE-5 (induced by source paper "permutation enhanced parallel reconstruction with a linear compressive sampling device"):
  with members:
    - "the restricted isometry property for random block diagonal matrices"

Connected through hyperedge HE-6 (induced by source paper "parallel compressed sensing reconstruction"):
  with members:
    - "the restricted isometry property for random block diagonal matrices"

Question: Please tell me which class the center node 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)