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 "robust coding of encrypted images via structural matrix"):
  Abstract: In this work, we intend to design a robust coder, based on compressive sensing with structurally random matrix, for encrypted images over packet transmission networks. The proposed coder can be applied in the scenario that Alice needs a semi-trusted channel provider Charlie to encode and transmit the encrypted image to Bob. In particular, Alice first encrypts an image using globally random permutation and then sends the encrypted image to Charlie who samples the encrypted image using a structural matrix. Through an imperfect channel with packet loss, Bob receives the compressive measurements and reconstructs the original image by joint decryption and decoding. Experimental results show that the proposed coder can be considered as an efficient multiple description coder with a number of descriptions against packet loss. HighlightsThis paper proposes a robust coder for encrypted images for the first time.The proposed coder is an efficient multiple description coder with many descriptions.The proposed coder can be applied to a particular application scenario.

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 "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "compressive measurements generated by structurally random matrices asymptotic normality and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg")
  Member "distribution of compressive measurements generated by structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint quantization and diffusion for compressed sensing measurements of natural images")
  Member "fast and efficient compressive sensing using structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")

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.CR(Cryptography and Security)