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: "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
  Abstract: Despite the linearity of its encoding, compressed sensing (CS) may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts). In this paper, we quantify by theoretical means the resistance of the least complex form of this kind of encoding against known-plaintext attacks. For both standard CS with antipodal random matrices and recent multiclass encryption schemes based on it, we show how the number of candidate encoding matrices that match a typical plaintext-ciphertext pair is so large that the search for the true encoding matrix inconclusive. Such results on the practical ineffectiveness of known-plaintext attacks underlie the fact that even closely related signal recovery under encoding matrix uncertainty is doomed to fail. Practical attacks are then exemplified by applying CS with antipodal random matrices as a multiclass encryption scheme to signals such as images and electrocardiographic tracks, showing that the extracted information on the true encoding matrix from a plaintext-ciphertext pair leads to no significant signal recovery quality increase. This theoretical and empirical evidence clarifies that, although not perfectly secure, both standard CS and multiclass encryption schemes feature a noteworthy level of security against known-plaintext attacks, therefore increasing its appeal as a negligible-cost encryption method for resource-limited sensing applications.

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"
    - "low complexity multiclass encryption by compressed sensing"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "a compressed sensing wire tap channel"

Connected through hyperedge HE-2 (induced by source paper "fast reconstruction algorithm for perturbed compressive sensing based on total least squares and proximal splitting"):
  with members:
    - "low complexity multiclass encryption by compressed sensing"
    - "bilinear generalized approximate message passing part i derivation"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "on the performance bound of sparse estimation with sensing matrix perturbation"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-3 (induced by source paper "bi level protected compressive sampling"):
  with members:
    - "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"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "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-4 (induced by source paper "robust coding of encrypted images via structural matrix"):
  with members:
    - "low complexity multiclass encryption by compressed sensing"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"
    - "distribution of compressive measurements generated by structurally random matrices"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-5 (induced by source paper "towards secure compressive sampling scheme"):
  with members:
    - "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"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "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"

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)